Show of Hands:Votes: Party classification:: Votes_Q_02_cluster

---

Date: (Mon) May 30, 2016

Introduction:

Data: Source: Training: “https://inclass.kaggle.com/c/can-we-predict-voting-outcomes/download/train2016.csv”
New: “https://inclass.kaggle.com/c/can-we-predict-voting-outcomes/download/test2016.csv”
Time period:

Synopsis:

Based on analysis utilizing <> techniques, :

Summary of key steps & error improvement stats:

Prediction Accuracy Enhancement Options:

• transform.data chunk:
  • derive features from multiple features
• manage.missing.data chunk:
  • Not fill missing vars
  • Fill missing numerics with a different algorithm
  • Fill missing chars with data based on clusters

[](.png)

Potential next steps include:

• Organization:
  • Categorize by chunk
  • Priority criteria:
    0. Ease of change
    1. Impacts report
    2. Cleans innards
    3. Bug report
• all chunks:
  • at chunk-end rm(!glb_)
• manage.missing.data chunk:
  • cleaner way to manage re-splitting of training vs. new entity
• extract.features chunk:
  • Add n-grams for glbFeatsText
    • “RTextTools”, “tau”, “RWeka”, and “textcat” packages
• fit.models chunk:
  • Classification: Plot AUC Curves for all models & highlight glbMdlSel
  • Prediction accuracy scatter graph:
  • Add tiles (raw vs. PCA)
  • Use shiny for drop-down of “important” features

  • Use plot.ly for interactive plots ?

  • Change .fit suffix of model metrics to .mdl if it’s data independent (e.g. AIC, Adj.R.Squared - is it truly data independent ?, etc.)
  • create a custom model for rpart that has minbucket as a tuning parameter

  • varImp for randomForest crashes in caret version:6.0.41 -> submit bug report

• Probability handling for multinomials vs. desired binomial outcome
• ROCR currently supports only evaluation of binary classification tasks (version 1.0.7)

• extensions toward multiclass classification are scheduled for the next release

• fit.all.training chunk:
  • myplot_prediction_classification: displays ‘x’ instead of ‘+’ when there are no prediction errors
• Compare glb_sel_mdl vs. glb_fin_mdl:
  • varImp
  • Prediction differences (shd be minimal ?)
• Move glb_analytics_diag_plots to mydsutils.R: (+) Easier to debug (-) Too many glb vars used
• Add print(ggplot.petrinet(glb_analytics_pn) + coord_flip()) at the end of every major chunk
• Parameterize glb_analytics_pn
• Move glb_impute_missing_data to mydsutils.R: (-) Too many glb vars used; glb_<>_df reassigned
• Do non-glm methods handle interaction terms ?
• f-score computation for classifiers should be summation across outcomes (not just the desired one ?)
• Add accuracy computation to glb_dmy_mdl in predict.data.new chunk
• Why does splitting fit.data.training.all chunk into separate chunks add an overhead of ~30 secs ? It’s not rbind b/c other chunks have lower elapsed time. Is it the number of plots ?
• Incorporate code chunks in print_sessionInfo
• Test against
  • projects in github.com/bdanalytics
  • lectures in jhu-datascience track

Analysis:

rm(list = ls())
set.seed(12345)
options(stringsAsFactors = FALSE)
source("~/Dropbox/datascience/R/mycaret.R")
source("~/Dropbox/datascience/R/mydsutils.R")

## Loading required package: caret

## Loading required package: lattice

## Loading required package: ggplot2

source("~/Dropbox/datascience/R/mypetrinet.R")
source("~/Dropbox/datascience/R/myplclust.R")
source("~/Dropbox/datascience/R/myplot.R")
source("~/Dropbox/datascience/R/myscript.R")
source("~/Dropbox/datascience/R/mytm.R")
# Gather all package requirements here
suppressPackageStartupMessages(require(doMC))
glbCores <- 6 # of cores on machine - 2
registerDoMC(glbCores) 

suppressPackageStartupMessages(require(caret))
require(plyr)

## Loading required package: plyr

require(dplyr)

## Loading required package: dplyr

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:plyr':
## 
##     arrange, count, desc, failwith, id, mutate, rename, summarise,
##     summarize

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

require(knitr)

## Loading required package: knitr

require(stringr)

## Loading required package: stringr

#source("dbgcaret.R")
#packageVersion("snow")
#require(sos); findFn("cosine", maxPages=2, sortby="MaxScore")

# Analysis control global variables
# Inputs
#   url/name = "<PathPointer>"; if url specifies a zip file, name = "<filename>"; 
#               or named collection of <PathPointer>s
#   sep = choose from c(NULL, "\t")
glbObsTrnFile <- list(url = "https://inclass.kaggle.com/c/can-we-predict-voting-outcomes/download/train2016.csv"
    # or list(url = c(NULL, <.inp1> = "<path1>", <.inp2> = "<path2>"))
    #, splitSpecs = list(method = "copy" # default when glbObsNewFile is NULL
    #                       select from c("copy", NULL ???, "condition", "sample", )
    #                      ,nRatio = 0.3 # > 0 && < 1 if method == "sample" 
    #                      ,seed = 123 # any integer or glbObsTrnPartitionSeed if method == "sample" 
    #                      ,condition = # or 'is.na(<var>)'; '<var> <condition_operator> <value>'    
    #                      )
    )                   
 
glbObsNewFile <- list(url = "https://inclass.kaggle.com/c/can-we-predict-voting-outcomes/download/test2016.csv") 

glbObsDropCondition <- NULL # : default
#   enclose in single-quotes b/c condition might include double qoutes
#       use | & ; NOT || &&    
#   '<condition>' 
    # 'grepl("^First Draft Video:", glbObsAll$Headline)'
    # 'is.na(glbObsAll[, glb_rsp_var_raw])'
    # '(is.na(glbObsAll[, glb_rsp_var_raw]) & grepl("Train", glbObsAll[, glbFeatsId]))'
    # 'is.na(strptime(glbObsAll[, "Date"], glbFeatsDateTime[["Date"]]["format"], tz = glbFeatsDateTime[["Date"]]["timezone"]))'
#nrow(do.call("subset",list(glbObsAll, parse(text=paste0("!(", glbObsDropCondition, ")")))))
    
glb_obs_repartition_train_condition <- NULL # : default
#    "<condition>" 

glb_max_fitobs <- NULL # or any integer
glbObsTrnPartitionSeed <- 123 # or any integer
                         
glb_is_regression <- FALSE; glb_is_classification <- !glb_is_regression; 
    glb_is_binomial <- TRUE # or TRUE or FALSE

glb_rsp_var_raw <- "Party"

# for classification, the response variable has to be a factor
glb_rsp_var <- "Party.fctr"

# if the response factor is based on numbers/logicals e.g (0/1 OR TRUE/FALSE vs. "A"/"B"), 
#   or contains spaces (e.g. "Not in Labor Force")
#   caret predict(..., type="prob") crashes
glb_map_rsp_raw_to_var <- #NULL 
function(raw) {
#     return(raw ^ 0.5)
#     return(log(raw))
#     return(log(1 + raw))
#     return(log10(raw)) 
#     return(exp(-raw / 2))
    ret_vals <- rep_len(NA, length(raw)); ret_vals[!is.na(raw)] <- ifelse(raw[!is.na(raw)] == "Republican", "R", "D"); return(relevel(as.factor(ret_vals), ref = "R"))
#     as.factor(paste0("B", raw))
#     as.factor(gsub(" ", "\\.", raw))
    }

#if glb_rsp_var_raw is numeric:
#print(summary(glbObsAll[, glb_rsp_var_raw]))
#glb_map_rsp_raw_to_var(tst <- c(NA, as.numeric(summary(glbObsAll[, glb_rsp_var_raw])))) 

#if glb_rsp_var_raw is character:
#print(table(glbObsAll[, glb_rsp_var_raw], useNA = "ifany"))
# print(table(glb_map_rsp_raw_to_var(tst <- glbObsAll[, glb_rsp_var_raw]), useNA = "ifany"))

glb_map_rsp_var_to_raw <- #NULL 
function(var) {
#     return(var ^ 2.0)
#     return(exp(var))
#     return(10 ^ var) 
#     return(-log(var) * 2)
#     as.numeric(var)
#     levels(var)[as.numeric(var)]
    sapply(levels(var)[as.numeric(var)], function(elm) 
        if (is.na(elm)) return(elm) else
        if (elm == 'R') return("Republican") else
        if (elm == 'D') return("Democrat") else
        stop("glb_map_rsp_var_to_raw: unexpected value: ", elm)
        )  
#     gsub("\\.", " ", levels(var)[as.numeric(var)])
#     c("<=50K", " >50K")[as.numeric(var)]
#     c(FALSE, TRUE)[as.numeric(var)]
}
# print(table(glb_map_rsp_var_to_raw(glb_map_rsp_raw_to_var(tst)), useNA = "ifany"))

if ((glb_rsp_var != glb_rsp_var_raw) && is.null(glb_map_rsp_raw_to_var))
    stop("glb_map_rsp_raw_to_var function expected")

# List info gathered for various columns
# <col_name>:   <description>; <notes>
# USER_ID - an anonymous id unique to a given user
# YOB - the year of birth of the user
# Gender - the gender of the user, either Male or Female
# Income - the household income of the user. Either not provided, or one of "under $25,000", "$25,001 - $50,000", "$50,000 - $74,999", "$75,000 - $100,000", "$100,001 - $150,000", or "over $150,000".
# HouseholdStatus - the household status of the user. Either not provided, or one of "Domestic Partners (no kids)", "Domestic Partners (w/kids)", "Married (no kids)", "Married (w/kids)", "Single (no kids)", or "Single (w/kids)".
# EducationalLevel - the education level of the user. Either not provided, or one of "Current K-12", "High School Diploma", "Current Undergraduate", "Associate's Degree", "Bachelor's Degree", "Master's Degree", or "Doctoral Degree".
# Party - the political party for whom the user intends to vote for. Either "Democrat" or "Republican
# Q124742, Q124122, . . . , Q96024 - 101 different questions that the users were asked on Show of Hands. If the user didn't answer the question, there is a blank. For information about the question text and possible answers, see the file Questions.pdf.

# currently does not handle more than 1 column; consider concatenating multiple columns
# If glbFeatsId == NULL, ".rownames <- as.numeric(row.names())" is the default
glbFeatsId <- "USER_ID" # choose from c(NULL : default, "<id_feat>") 
glbFeatsCategory <- "Hhold.fctr" # choose from c(NULL : default, "<category_feat>")
# glbFeatsCategory <- "Q109244.fctr" # choose from c(NULL : default, "<category_feat>") -> OOB performed worse than "Hhold.fctr"

# User-specified exclusions
glbFeatsExclude <- c(NULL
#   Feats that shd be excluded due to known causation by prediction variable
# , "<feat1", "<feat2>"
#   Feats that are factors with unique values (as % of nObs) > 49 (empirically derived)
#   Feats that are linear combinations (alias in glm)
#   Feature-engineering phase -> start by excluding all features except id & category & 
#       work each one in
    , "USER_ID", "YOB", "Gender", "Income", "HouseholdStatus", "EducationLevel" 
    ,"Q124742","Q124122" 
    ,"Q123621","Q123464"
    ,"Q122771","Q122770","Q122769","Q122120"
    ,"Q121700","Q121699","Q121011"
    ,"Q120978","Q120650","Q120472","Q120379","Q120194","Q120014","Q120012" 
    ,"Q119851","Q119650","Q119334"
    ,"Q118892","Q118237","Q118233","Q118232","Q118117"
    ,"Q117193","Q117186"
    ,"Q116797","Q116881","Q116953","Q116601","Q116441","Q116448","Q116197"
    ,"Q115602","Q115777","Q115610","Q115611","Q115899","Q115390","Q115195"
    ,"Q114961","Q114748","Q114517","Q114386","Q114152"
    ,"Q113992","Q113583","Q113584","Q113181"
    ,"Q112478","Q112512","Q112270"
    ,"Q111848","Q111580","Q111220"
    ,"Q110740"
    ,"Q109367","Q109244"
    ,"Q108950","Q108855","Q108617","Q108856","Q108754","Q108342","Q108343"
    ,"Q107869","Q107491"
    ,"Q106993","Q106997","Q106272","Q106388","Q106389","Q106042"
    ,"Q105840","Q105655"
    ,"Q104996"
    ,"Q103293"
    ,"Q102906","Q102674","Q102687","Q102289","Q102089"
    ,"Q101162","Q101163","Q101596"
    ,"Q100689","Q100680","Q100562","Q100010"
    ,"Q99982"
    ,"Q99716"
    ,"Q99581"
    ,"Q99480"
    ,"Q98869"
    ,"Q98578"
    ,"Q98197"
    ,"Q98059","Q98078"
    ,"Q96024" # Done
    ,".pos") 
if (glb_rsp_var_raw != glb_rsp_var)
    glbFeatsExclude <- union(glbFeatsExclude, glb_rsp_var_raw)                    

glbFeatsInteractionOnly <- list()
#glbFeatsInteractionOnly[["<child_feat>"]] <- "<parent_feat>"

glbFeatsDrop <- c(NULL
                # , "<feat1>", "<feat2>"
                )

glb_map_vars <- NULL # or c("<var1>", "<var2>")
glb_map_urls <- list();
# glb_map_urls[["<var1>"]] <- "<var1.url>"

# Derived features; Use this mechanism to cleanse data ??? Cons: Data duplication ???
glbFeatsDerive <- list();

# glbFeatsDerive[["<feat.my.sfx>"]] <- list(
#     mapfn = function(<arg1>, <arg2>) { return(function(<arg1>, <arg2>)) } 
#   , args = c("<arg1>", "<arg2>"))
#myprint_df(data.frame(ImageId = mapfn(glbObsAll$.src, glbObsAll$.pos)))
#data.frame(ImageId = mapfn(glbObsAll$.src, glbObsAll$.pos))[7045:7055, ]

    # character
#     mapfn = function(Education) { raw <- Education; raw[is.na(raw)] <- "NA.my"; return(as.factor(raw)) } 
#     mapfn = function(Week) { return(substr(Week, 1, 10)) }
#     mapfn = function(Name) { return(sapply(Name, function(thsName) 
#                                             str_sub(unlist(str_split(thsName, ","))[1], 1, 1))) } 

#     mapfn = function(descriptor) { return(plyr::revalue(descriptor, c(
#         "ABANDONED BUILDING"  = "OTHER",
#         "**"                  = "**"
#                                           ))) }

#     mapfn = function(description) { mod_raw <- description;
    # This is here because it does not work if it's in txt_map_filename
#         mod_raw <- gsub(paste0(c("\n", "\211", "\235", "\317", "\333"), collapse = "|"), " ", mod_raw)
    # Don't parse for "." because of ".com"; use customized gsub for that text
#         mod_raw <- gsub("(\\w)(!|\\*|,|-|/)(\\w)", "\\1\\2 \\3", mod_raw);
    # Some state acrnoyms need context for separation e.g. 
    #   LA/L.A. could either be "Louisiana" or "LosAngeles"
        # modRaw <- gsub("\\bL\\.A\\.( |,|')", "LosAngeles\\1", modRaw);
    #   OK/O.K. could either be "Oklahoma" or "Okay"
#         modRaw <- gsub("\\bACA OK\\b", "ACA OKay", modRaw); 
#         modRaw <- gsub("\\bNow O\\.K\\.\\b", "Now OKay", modRaw);        
    #   PR/P.R. could either be "PuertoRico" or "Public Relations"        
        # modRaw <- gsub("\\bP\\.R\\. Campaign", "PublicRelations Campaign", modRaw);        
    #   VA/V.A. could either be "Virginia" or "VeteransAdministration"        
        # modRaw <- gsub("\\bthe V\\.A\\.\\:", "the VeteranAffairs:", modRaw);
    #   
    # Custom mods

#         return(mod_raw) }

    # numeric
# Create feature based on record position/id in data   
glbFeatsDerive[[".pos"]] <- list(
    mapfn = function(raw1) { return(1:length(raw1)) }
    , args = c(".rnorm"))
# glbFeatsDerive[[".pos.y"]] <- list(
#     mapfn = function(raw1) { return(1:length(raw1)) }       
#     , args = c(".rnorm"))    

# Add logs of numerics that are not distributed normally
#   Derive & keep multiple transformations of the same feature, if normality is hard to achieve with just one transformation
#   Right skew: logp1; sqrt; ^ 1/3; logp1(logp1); log10; exp(-<feat>/constant)
# glbFeatsDerive[["WordCount.log1p"]] <- list(
#     mapfn = function(WordCount) { return(log1p(WordCount)) } 
#   , args = c("WordCount"))
# glbFeatsDerive[["WordCount.root2"]] <- list(
#     mapfn = function(WordCount) { return(WordCount ^ (1/2)) } 
#   , args = c("WordCount"))
# glbFeatsDerive[["WordCount.nexp"]] <- list(
#     mapfn = function(WordCount) { return(exp(-WordCount)) } 
#   , args = c("WordCount"))
#print(summary(glbObsAll$WordCount))
#print(summary(mapfn(glbObsAll$WordCount)))
    
# If imputation shd be skipped for this feature
# glbFeatsDerive[["District.fctr"]] <- list(
#     mapfn = function(District) {
#         raw <- District;
#         ret_vals <- rep_len("NA", length(raw)); 
#         ret_vals[!is.na(raw)] <- sapply(raw[!is.na(raw)], function(elm) 
#                                         ifelse(elm < 10, "1-9", 
#                                         ifelse(elm < 20, "10-19", "20+")));
#         return(relevel(as.factor(ret_vals), ref = "NA"))
#     }       
#     , args = c("District"))    

# YOB options:
# 1. Missing data:
# 1.1   0 -> Does not improve baseline
# 1.2   Cut factors & "NA" is a level
# 2. Data corrections: < 1928 & > 2000
# 3. Scale YOB
# 4. Add Age
glbFeatsDerive[["YOB.Age.fctr"]] <- list(
    mapfn = function(raw1) {
        raw <- 2016 - raw1 
        # raw[!is.na(raw) & raw >= 2010] <- NA
        raw[!is.na(raw) & (raw <= 15)] <- NA
        raw[!is.na(raw) & (raw >= 90)] <- NA        
        retVal <- rep_len("NA", length(raw))
        # breaks = c(1879, seq(1949, 1989, 10), 2049)
        # cutVal <- cut(raw[!is.na(raw)], breaks = breaks, 
        #               labels = as.character(breaks + 1)[1:(length(breaks) - 1)])
        cutVal <- cut(raw[!is.na(raw)], breaks = c(15, 20, 25, 30, 35, 40, 50, 65, 90))
        retVal[!is.na(raw)] <- levels(cutVal)[cutVal]
        return(factor(retVal, levels = c("NA"
                ,"(15,20]","(20,25]","(25,30]","(30,35]","(35,40]","(40,50]","(50,65]","(65,90]"),
                        ordered = TRUE))
    }
    , args = c("YOB"))

glbFeatsDerive[["Gender.fctr"]] <- list(
    mapfn = function(raw1) {
        raw <- raw1
        raw[raw %in% ""] <- "N"
        raw <- gsub("Male"  , "M", raw, fixed = TRUE)
        raw <- gsub("Female", "F", raw, fixed = TRUE)        
        return(relevel(as.factor(raw), ref = "N"))
    }
    , args = c("Gender"))

glbFeatsDerive[["Income.fctr"]] <- list(
    mapfn = function(raw1) { raw <- raw1;
        raw[raw %in% ""] <- "N"
        raw <- gsub("under $25,000"      , "<25K"    , raw, fixed = TRUE)
        raw <- gsub("$25,001 - $50,000"  , "25-50K"  , raw, fixed = TRUE)
        raw <- gsub("$50,000 - $74,999"  , "50-75K"  , raw, fixed = TRUE)
        raw <- gsub("$75,000 - $100,000" , "75-100K" , raw, fixed = TRUE)        
        raw <- gsub("$100,001 - $150,000", "100-150K", raw, fixed = TRUE)
        raw <- gsub("over $150,000"      , ">150K"   , raw, fixed = TRUE)        
        return(factor(raw, levels = c("N","<25K","25-50K","50-75K","75-100K","100-150K",">150K"),
                      ordered = TRUE))
    }
    , args = c("Income"))

glbFeatsDerive[["Hhold.fctr"]] <- list(
    mapfn = function(raw1) { raw <- raw1;
        raw[raw %in% ""] <- "N"
        raw <- gsub("Domestic Partners (no kids)", "PKn", raw, fixed = TRUE)
        raw <- gsub("Domestic Partners (w/kids)" , "PKy", raw, fixed = TRUE)        
        raw <- gsub("Married (no kids)"          , "MKn", raw, fixed = TRUE)
        raw <- gsub("Married (w/kids)"           , "MKy", raw, fixed = TRUE)        
        raw <- gsub("Single (no kids)"           , "SKn", raw, fixed = TRUE)
        raw <- gsub("Single (w/kids)"            , "SKy", raw, fixed = TRUE)        
        return(relevel(as.factor(raw), ref = "N"))
    }
    , args = c("HouseholdStatus"))

glbFeatsDerive[["Edn.fctr"]] <- list(
    mapfn = function(raw1) { raw <- raw1;
        raw[raw %in% ""] <- "N"
        raw <- gsub("Current K-12"         , "K12", raw, fixed = TRUE)
        raw <- gsub("High School Diploma"  , "HSD", raw, fixed = TRUE)        
        raw <- gsub("Current Undergraduate", "CCg", raw, fixed = TRUE)
        raw <- gsub("Associate's Degree"   , "Ast", raw, fixed = TRUE)
        raw <- gsub("Bachelor's Degree"    , "Bcr", raw, fixed = TRUE)        
        raw <- gsub("Master's Degree"      , "Msr", raw, fixed = TRUE)
        raw <- gsub("Doctoral Degree"      , "PhD", raw, fixed = TRUE)        
        return(factor(raw, levels = c("N","K12","HSD","CCg","Ast","Bcr","Msr","PhD"),
                      ordered = TRUE))
    }
    , args = c("EducationLevel"))

# for (qsn in c("Q124742","Q124122"))
# for (qsn in grep("Q12(.{4})(?!\\.fctr)", names(glbObsTrn), value = TRUE, perl = TRUE))
for (qsn in grep("Q", glbFeatsExclude, fixed = TRUE, value = TRUE))    
    glbFeatsDerive[[paste0(qsn, ".fctr")]] <- list(
        mapfn = function(raw1) {
            raw1[raw1 %in% ""] <- "NA"
            rawVal <- unique(raw1)
            
            if (length(setdiff(rawVal, (expVal <- c("NA", "No", "Ys")))) == 0) {
                raw1 <- gsub("Yes", "Ys", raw1, fixed = TRUE)
                if (length(setdiff(rawVal, expVal)) > 0)
                    stop(qsn, " vals: ", paste0(rawVal, collapse = "|"), 
                         " does not match expectation: ", paste0(expVal, collapse = "|"))
            } else
            if (length(setdiff(rawVal, (expVal <- c("NA", "Me", "Circumstances")))) == 0) {
                raw1 <- gsub("Circumstances", "Cs", raw1, fixed = TRUE)
                if (length(setdiff(rawVal, expVal)) > 0)
                    stop(qsn, " vals: ", paste0(rawVal, collapse = "|"), 
                         " does not match expectation: ", paste0(expVal, collapse = "|"))
            } else
            if (length(setdiff(rawVal, (expVal <- c("NA", "Grrr people", "Yay people!")))) == 0) {
                raw1 <- gsub("Grrr people", "Gr", raw1, fixed = TRUE)
                raw1 <- gsub("Yay people!", "Yy", raw1, fixed = TRUE)                
                if (length(setdiff(rawVal, expVal)) > 0)
                    stop(qsn, " vals: ", paste0(rawVal, collapse = "|"), 
                         " does not match expectation: ", paste0(expVal, collapse = "|"))
            } else
            if (length(setdiff(rawVal, (expVal <- c("NA", "Idealist", "Pragmatist")))) == 0) {
                raw1 <- gsub("Idealist"  , "Id", raw1, fixed = TRUE)
                raw1 <- gsub("Pragmatist", "Pr", raw1, fixed = TRUE)                
                if (length(setdiff(rawVal, expVal)) > 0)
                    stop(qsn, " vals: ", paste0(rawVal, collapse = "|"), 
                         " does not match expectation: ", paste0(expVal, collapse = "|"))
            } else
            if (length(setdiff(rawVal, (expVal <- c("NA", "Private", "Public")))) == 0) {
                raw1 <- gsub("Private", "Pt", raw1, fixed = TRUE)
                raw1 <- gsub("Public" , "Pc", raw1, fixed = TRUE)                
                if (length(setdiff(rawVal, expVal)) > 0)
                    stop(qsn, " vals: ", paste0(rawVal, collapse = "|"), 
                         " does not match expectation: ", paste0(expVal, collapse = "|"))
            }
            
            return(relevel(as.factor(raw1), ref = "NA"))
        }
        , args = c(qsn))

# If imputation of missing data is not working ...
# glbFeatsDerive[["FertilityRate.nonNA"]] <- list(
#     mapfn = function(FertilityRate, Region) {
#         RegionMdn <- tapply(FertilityRate, Region, FUN = median, na.rm = TRUE)
# 
#         retVal <- FertilityRate
#         retVal[is.na(FertilityRate)] <- RegionMdn[Region[is.na(FertilityRate)]]
#         return(retVal)
#     }
#     , args = c("FertilityRate", "Region"))
    
#     mapfn = function(HOSPI.COST) { return(cut(HOSPI.COST, 5, breaks = c(0, 100000, 200000, 300000, 900000), labels = NULL)) }     
#     mapfn = function(Rasmussen)  { return(ifelse(sign(Rasmussen) >= 0, 1, 0)) } 
#     mapfn = function(startprice) { return(startprice ^ (1/2)) }       
#     mapfn = function(startprice) { return(log(startprice)) }   
#     mapfn = function(startprice) { return(exp(-startprice / 20)) }
#     mapfn = function(startprice) { return(scale(log(startprice))) }     
#     mapfn = function(startprice) { return(sign(sprice.predict.diff) * (abs(sprice.predict.diff) ^ (1/10))) }        

    # factor      
#     mapfn = function(PropR) { return(as.factor(ifelse(PropR >= 0.5, "Y", "N"))) }
#     mapfn = function(productline, description) { as.factor(gsub(" ", "", productline)) }
#     mapfn = function(purpose) { return(relevel(as.factor(purpose), ref="all_other")) }
#     mapfn = function(raw) { tfr_raw <- as.character(cut(raw, 5)); 
#                             tfr_raw[is.na(tfr_raw)] <- "NA.my";
#                             return(as.factor(tfr_raw)) }
#     mapfn = function(startprice.log10) { return(cut(startprice.log10, 3)) }
#     mapfn = function(startprice.log10) { return(cut(sprice.predict.diff, c(-1000, -100, -10, -1, 0, 1, 10, 100, 1000))) }    

#     , args = c("<arg1>"))
    
    # multiple args
#     mapfn = function(id, date) { return(paste(as.character(id), as.character(date), sep = "#")) }        
#     mapfn = function(PTS, oppPTS) { return(PTS - oppPTS) }
#     mapfn = function(startprice.log10.predict, startprice) {
#                  return(spdiff <- (10 ^ startprice.log10.predict) - startprice) } 
#     mapfn = function(productline, description) { as.factor(
#         paste(gsub(" ", "", productline), as.numeric(nchar(description) > 0), sep = "*")) }
#     mapfn = function(.src, .pos) { 
#         return(paste(.src, sprintf("%04d", 
#                                    ifelse(.src == "Train", .pos, .pos - 7049)
#                                    ), sep = "#")) }       

# # If glbObsAll is not sorted in the desired manner
#     mapfn=function(Week) { return(coredata(lag(zoo(orderBy(~Week, glbObsAll)$ILI), -2, na.pad=TRUE))) }
#     mapfn=function(ILI) { return(coredata(lag(zoo(ILI), -2, na.pad=TRUE))) }
#     mapfn=function(ILI.2.lag) { return(log(ILI.2.lag)) }

# glbFeatsDerive[["<var1>"]] <- glbFeatsDerive[["<var2>"]]

# tst <- "descr.my"; args_lst <- NULL; for (arg in glbFeatsDerive[[tst]]$args) args_lst[[arg]] <- glbObsAll[, arg]; print(head(args_lst[[arg]])); print(head(drv_vals <- do.call(glbFeatsDerive[[tst]]$mapfn, args_lst))); 
# print(which_ix <- which(args_lst[[arg]] == 0.75)); print(drv_vals[which_ix]); 

glbFeatsDateTime <- list()
# Use OlsonNames() to enumerate supported time zones
# glbFeatsDateTime[["<DateTimeFeat>"]] <- 
#     c(format = "%Y-%m-%d %H:%M:%S" or "%m/%e/%y", timezone = "US/Eastern", impute.na = TRUE, 
#       last.ctg = FALSE, poly.ctg = FALSE)

glbFeatsPrice <- NULL # or c("<price_var>")

glbFeatsImage <- list() #list(<imageFeat> = list(patchSize = 10)) # if patchSize not specified, no patch computation

glbFeatsText <- list()
Sys.setlocale("LC_ALL", "C") # For english

## [1] "C/C/C/C/C/en_US.UTF-8"

#glbFeatsText[["<TextFeature>"]] <- list(NULL,
#   ,names = myreplacePunctuation(str_to_lower(gsub(" ", "", c(NULL, 
#       <comma-separated-screened-names>
#   ))))
#   ,rareWords = myreplacePunctuation(str_to_lower(gsub(" ", "", c(NULL, 
#       <comma-separated-nonSCOWL-words>
#   ))))
#)

# Text Processing Step: custom modifications not present in txt_munge -> use glbFeatsDerive
# Text Processing Step: universal modifications
glb_txt_munge_filenames_pfx <- "<projectId>_mytxt_"

# Text Processing Step: tolower
# Text Processing Step: myreplacePunctuation
# Text Processing Step: removeWords
glb_txt_stop_words <- list()
# Remember to use unstemmed words
if (length(glbFeatsText) > 0) {
    require(tm)
    require(stringr)

    glb_txt_stop_words[["<txt_var>"]] <- sort(myreplacePunctuation(str_to_lower(gsub(" ", "", c(NULL
        # Remove any words from stopwords            
#         , setdiff(myreplacePunctuation(stopwords("english")), c("<keep_wrd1>", <keep_wrd2>"))
                                
        # Remove salutations
        ,"mr","mrs","dr","Rev"                                

        # Remove misc
        #,"th" # Happy [[:digit::]]+th birthday 

        # Remove terms present in Trn only or New only; search for "Partition post-stem"
        #   ,<comma-separated-terms>        

        # cor.y.train == NA
#         ,unlist(strsplit(paste(c(NULL
#           ,"<comma-separated-terms>"
#         ), collapse=",")

        # freq == 1; keep c("<comma-separated-terms-to-keep>")
            # ,<comma-separated-terms>

        # chisq.pval high (e.g. == 1); keep c("<comma-separated-terms-to-keep>")
            # ,<comma-separated-terms>

        # nzv.freqRatio high (e.g. >= glbFeatsNzvFreqMax); keep c("<comma-separated-terms-to-keep>")
            # ,<comma-separated-terms>        
                                            )))))
}
#orderBy(~term, glb_post_stem_words_terms_df_lst[[txtFeat]][grep("^man", glb_post_stem_words_terms_df_lst[[txtFeat]]$term), ])
#glbObsAll[glb_post_stem_words_terms_mtrx_lst[[txtFeat]][, 4866] > 0, c(glb_rsp_var, txtFeat)]

# To identify terms with a specific freq
#paste0(sort(subset(glb_post_stop_words_terms_df_lst[[txtFeat]], freq == 1)$term), collapse = ",")
#paste0(sort(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], freq <= 2)$term), collapse = ",")
#subset(glb_post_stem_words_terms_df_lst[[txtFeat]], term %in% c("zinger"))

# To identify terms with a specific freq & 
#   are not stemmed together later OR is value of color.fctr (e.g. gold)
#paste0(sort(subset(glb_post_stop_words_terms_df_lst[[txtFeat]], (freq == 1) & !(term %in% c("blacked","blemish","blocked","blocks","buying","cables","careful","carefully","changed","changing","chargers","cleanly","cleared","connect","connects","connected","contains","cosmetics","default","defaulting","defective","definitely","describe","described","devices","displays","drop","drops","engravement","excellant","excellently","feels","fix","flawlessly","frame","framing","gentle","gold","guarantee","guarantees","handled","handling","having","install","iphone","iphones","keeped","keeps","known","lights","line","lining","liquid","liquidation","looking","lots","manuals","manufacture","minis","most","mostly","network","networks","noted","opening","operated","performance","performs","person","personalized","photograph","physically","placed","places","powering","pre","previously","products","protection","purchasing","returned","rotate","rotation","running","sales","second","seconds","shipped","shuts","sides","skin","skinned","sticker","storing","thats","theres","touching","unusable","update","updates","upgrade","weeks","wrapped","verified","verify") ))$term), collapse = ",")

#print(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], (freq <= 2)))
#glbObsAll[which(terms_mtrx[, 229] > 0), glbFeatsText]

# To identify terms with cor.y == NA
#orderBy(~-freq+term, subset(glb_post_stop_words_terms_df_lst[[txtFeat]], is.na(cor.y)))
#paste(sort(subset(glb_post_stop_words_terms_df_lst[[txtFeat]], is.na(cor.y))[, "term"]), collapse=",")
#orderBy(~-freq+term, subset(glb_post_stem_words_terms_df_lst[[txtFeat]], is.na(cor.y)))

# To identify terms with low cor.y.abs
#head(orderBy(~cor.y.abs+freq+term, subset(glb_post_stem_words_terms_df_lst[[txtFeat]], !is.na(cor.y))), 5)

# To identify terms with high chisq.pval
#subset(glb_post_stem_words_terms_df_lst[[txtFeat]], chisq.pval > 0.99)
#paste0(sort(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], (chisq.pval > 0.99) & (freq <= 10))$term), collapse=",")
#paste0(sort(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], (chisq.pval > 0.9))$term), collapse=",")
#head(orderBy(~-chisq.pval+freq+term, glb_post_stem_words_terms_df_lst[[txtFeat]]), 5)
#glbObsAll[glb_post_stem_words_terms_mtrx_lst[[txtFeat]][, 68] > 0, glbFeatsText]
#orderBy(~term, glb_post_stem_words_terms_df_lst[[txtFeat]][grep("^m", glb_post_stem_words_terms_df_lst[[txtFeat]]$term), ])

# To identify terms with high nzv.freqRatio
#summary(glb_post_stem_words_terms_df_lst[[txtFeat]]$nzv.freqRatio)
#paste0(sort(setdiff(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], (nzv.freqRatio >= glbFeatsNzvFreqMax) & (freq < 10) & (chisq.pval >= 0.05))$term, c( "128gb","3g","4g","gold","ipad1","ipad3","ipad4","ipadair2","ipadmini2","manufactur","spacegray","sprint","tmobil","verizon","wifion"))), collapse=",")

# To identify obs with a txt term
#tail(orderBy(~-freq+term, glb_post_stop_words_terms_df_lst[[txtFeat]]), 20)
#mydspObs(list(descr.my.contains="non"), cols=c("color", "carrier", "cellular", "storage"))
#grep("ever", dimnames(terms_stop_mtrx)$Terms)
#which(terms_stop_mtrx[, grep("ipad", dimnames(terms_stop_mtrx)$Terms)] > 0)
#glbObsAll[which(terms_stop_mtrx[, grep("16", dimnames(terms_stop_mtrx)$Terms)[1]] > 0), c(glbFeatsCategory, "storage", txtFeat)]

# Text Processing Step: screen for names # Move to glbFeatsText specs section in order of text processing steps
# glbFeatsText[["<txtFeat>"]]$names <- myreplacePunctuation(str_to_lower(gsub(" ", "", c(NULL
#         # Person names for names screening
#         ,<comma-separated-list>
#         
#         # Company names
#         ,<comma-separated-list>
#                     
#         # Product names
#         ,<comma-separated-list>
#     ))))

# glbFeatsText[["<txtFeat>"]]$rareWords <- myreplacePunctuation(str_to_lower(gsub(" ", "", c(NULL
#         # Words not in SCOWL db
#         ,<comma-separated-list>
#     ))))

# To identify char vectors post glbFeatsTextMap
#grep("six(.*)hour", glb_txt_chr_lst[[txtFeat]], ignore.case = TRUE, value = TRUE)
#grep("[S|s]ix(.*)[H|h]our", glb_txt_chr_lst[[txtFeat]], value = TRUE)

# To identify whether terms shd be synonyms
#orderBy(~term, glb_post_stop_words_terms_df_lst[[txtFeat]][grep("^moder", glb_post_stop_words_terms_df_lst[[txtFeat]]$term), ])
# term_row_df <- glb_post_stop_words_terms_df_lst[[txtFeat]][grep("^came$", glb_post_stop_words_terms_df_lst[[txtFeat]]$term), ]
# 
# cor(glb_post_stop_words_terms_mtrx_lst[[txtFeat]][glbObsAll$.lcn == "Fit", term_row_df$pos], glbObsTrn[, glb_rsp_var], use="pairwise.complete.obs")

# To identify which stopped words are "close" to a txt term
#sort(glbFeatsCluster)

# Text Processing Step: stemDocument
# To identify stemmed txt terms
#glb_post_stop_words_terms_df_lst[[txtFeat]][grep("^la$", glb_post_stop_words_terms_df_lst[[txtFeat]]$term), ]
#orderBy(~term, glb_post_stem_words_terms_df_lst[[txtFeat]][grep("^con", glb_post_stem_words_terms_df_lst[[txtFeat]]$term), ])
#glbObsAll[which(terms_stem_mtrx[, grep("use", dimnames(terms_stem_mtrx)$Terms)[[1]]] > 0), c(glbFeatsId, "productline", txtFeat)]
#glbObsAll[which(TfIdf_stem_mtrx[, 191] > 0), c(glbFeatsId, glbFeatsCategory, txtFeat)]
#glbObsAll[which(glb_post_stop_words_terms_mtrx_lst[[txtFeat]][, 6165] > 0), c(glbFeatsId, glbFeatsCategory, txtFeat)]
#which(glbObsAll$UniqueID %in% c(11915, 11926, 12198))

# Text Processing Step: mycombineSynonyms
#   To identify which terms are associated with not -> combine "could not" & "couldn't"
#findAssocs(glb_full_DTM_lst[[txtFeat]], "not", 0.05)
#   To identify which synonyms should be combined
#orderBy(~term, glb_post_stem_words_terms_df_lst[[txtFeat]][grep("^c", glb_post_stem_words_terms_df_lst[[txtFeat]]$term), ])
chk_comb_cor <- function(syn_lst) {
#     cor(terms_stem_mtrx[glbObsAll$.src == "Train", grep("^(damag|dent|ding)$", dimnames(terms_stem_mtrx)[[2]])], glbObsTrn[, glb_rsp_var], use="pairwise.complete.obs")
    print(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], term %in% syn_lst$syns))
    print(subset(get_corpus_terms(tm_map(glbFeatsTextCorpus[[txtFeat]], mycombineSynonyms, list(syn_lst), lazy=FALSE)), term == syn_lst$word))
#     cor(terms_stop_mtrx[glbObsAll$.src == "Train", grep("^(damage|dent|ding)$", dimnames(terms_stop_mtrx)[[2]])], glbObsTrn[, glb_rsp_var], use="pairwise.complete.obs")
#     cor(rowSums(terms_stop_mtrx[glbObsAll$.src == "Train", grep("^(damage|dent|ding)$", dimnames(terms_stop_mtrx)[[2]])]), glbObsTrn[, glb_rsp_var], use="pairwise.complete.obs")
}
#chk_comb_cor(syn_lst=list(word="cabl",  syns=c("cabl", "cord")))
#chk_comb_cor(syn_lst=list(word="damag",  syns=c("damag", "dent", "ding")))
#chk_comb_cor(syn_lst=list(word="dent",  syns=c("dent", "ding")))
#chk_comb_cor(syn_lst=list(word="use",  syns=c("use", "usag")))

glbFeatsTextSynonyms <- list()
# list parsed to collect glbFeatsText[[<txtFeat>]]$vldTerms
# glbFeatsTextSynonyms[["Hdln.my"]] <- list(NULL
#     # people in places
#     , list(word = "australia", syns = c("australia", "australian"))
#     , list(word = "italy", syns = c("italy", "Italian"))
#     , list(word = "newyork", syns = c("newyork", "newyorker"))    
#     , list(word = "Pakistan", syns = c("Pakistan", "Pakistani"))    
#     , list(word = "peru", syns = c("peru", "peruvian"))
#     , list(word = "qatar", syns = c("qatar", "qatari"))
#     , list(word = "scotland", syns = c("scotland", "scotish"))
#     , list(word = "Shanghai", syns = c("Shanghai", "Shanzhai"))    
#     , list(word = "venezuela", syns = c("venezuela", "venezuelan"))    
# 
#     # companies - needs to be data dependent 
#     #   - e.g. ensure BNP in this experiment/feat always refers to BNPParibas
#         
#     # general synonyms
#     , list(word = "Create", syns = c("Create","Creator")) 
#     , list(word = "cute", syns = c("cute","cutest"))     
#     , list(word = "Disappear", syns = c("Disappear","Fadeout"))     
#     , list(word = "teach", syns = c("teach", "taught"))     
#     , list(word = "theater",  syns = c("theater", "theatre", "theatres")) 
#     , list(word = "understand",  syns = c("understand", "understood"))    
#     , list(word = "weak",  syns = c("weak", "weaken", "weaker", "weakest"))
#     , list(word = "wealth",  syns = c("wealth", "wealthi"))    
#     
#     # custom synonyms (phrases)
#     
#     # custom synonyms (names)
#                                       )
#glbFeatsTextSynonyms[["<txtFeat>"]] <- list(NULL
#     , list(word="<stem1>",  syns=c("<stem1>", "<stem1_2>"))
#                                       )

for (txtFeat in names(glbFeatsTextSynonyms))
    for (entryIx in 1:length(glbFeatsTextSynonyms[[txtFeat]])) {
        glbFeatsTextSynonyms[[txtFeat]][[entryIx]]$word <-
            str_to_lower(glbFeatsTextSynonyms[[txtFeat]][[entryIx]]$word)
        glbFeatsTextSynonyms[[txtFeat]][[entryIx]]$syns <-
            str_to_lower(glbFeatsTextSynonyms[[txtFeat]][[entryIx]]$syns)        
    }        

glbFeatsTextSeed <- 181
# tm options include: check tm::weightSMART 
glb_txt_terms_control <- list( # Gather model performance & run-time stats
                    # weighting = function(x) weightSMART(x, spec = "nnn")
                    # weighting = function(x) weightSMART(x, spec = "lnn")
                    # weighting = function(x) weightSMART(x, spec = "ann")
                    # weighting = function(x) weightSMART(x, spec = "bnn")
                    # weighting = function(x) weightSMART(x, spec = "Lnn")
                    # 
                    weighting = function(x) weightSMART(x, spec = "ltn") # default
                    # weighting = function(x) weightSMART(x, spec = "lpn")                    
                    # 
                    # weighting = function(x) weightSMART(x, spec = "ltc")                    
                    # 
                    # weighting = weightBin 
                    # weighting = weightTf 
                    # weighting = weightTfIdf # : default
                # termFreq selection criteria across obs: tm default: list(global=c(1, Inf))
                    , bounds = list(global = c(1, Inf)) 
                # wordLengths selection criteria: tm default: c(3, Inf)
                    , wordLengths = c(1, Inf) 
                              ) 

glb_txt_cor_var <- glb_rsp_var # : default # or c(<feat>)

# select one from c("union.top.val.cor", "top.cor", "top.val", default: "top.chisq", "sparse")
glbFeatsTextFilter <- "top.chisq" 
glbFeatsTextTermsMax <- rep(10, length(glbFeatsText)) # :default
names(glbFeatsTextTermsMax) <- names(glbFeatsText)

# Text Processing Step: extractAssoc
glbFeatsTextAssocCor <- rep(1, length(glbFeatsText)) # :default 
names(glbFeatsTextAssocCor) <- names(glbFeatsText)

# Remember to use stemmed terms
glb_important_terms <- list()

# Text Processing Step: extractPatterns (ngrams)
glbFeatsTextPatterns <- list()
#glbFeatsTextPatterns[[<txtFeat>>]] <- list()
#glbFeatsTextPatterns[[<txtFeat>>]] <- c(metropolitan.diary.colon = "Metropolitan Diary:")

# Have to set it even if it is not used
# Properties:
#   numrows(glb_feats_df) << numrows(glbObsFit
#   Select terms that appear in at least 0.2 * O(FP/FN(glbObsOOB)) ???
#       numrows(glbObsOOB) = 1.1 * numrows(glbObsNew) ???
glb_sprs_thresholds <- NULL # or c(<txtFeat1> = 0.988, <txtFeat2> = 0.970, <txtFeat3> = 0.970)

glbFctrMaxUniqVals <- 20 # default: 20
glb_impute_na_data <- FALSE # or TRUE
glb_mice_complete.seed <- 144 # or any integer

glbFeatsCluster <- paste(grep("^Q.", glbFeatsExclude, value = TRUE), "fctr", sep = ".") # NULL : default or c("<feat1>", "<feat2>")
# glbFeatsCluster <- grep(paste0("[", 
#                         toupper(paste0(substr(glbFeatsText, 1, 1), collapse = "")),
#                                       "]\\.[PT]\\."), 
#                                names(glbObsAll), value = TRUE)

glb_cluster.seed <- 189 # or any integer
glbClusterEntropyVar <- NULL # c(glb_rsp_var, as.factor(cut(glb_rsp_var, 3)), default: NULL)
glbFeatsTextClusterVarsExclude <- FALSE # default FALSE

glb_interaction_only_feats <- NULL # : default or c(<parent_feat> = "<child_feat>")

glbFeatsNzvFreqMax <- 19 # 19 : caret default
glbFeatsNzvUniqMin <- 10 # 10 : caret default

glbRFESizes <- list()
#glbRFESizes[["mdlFamily"]] <- c(4, 8, 16, 32, 64, 67, 68, 69) # Accuracy@69/70 = 0.8258

glbObsFitOutliers <- list()
# If outliers.n >= 10; consider concatenation of interaction vars
# glbObsFitOutliers[["<mdlFamily>"]] <- c(NULL
#     is.na(.rstudent)
#     max(.rstudent)
#     is.na(.dffits)
#     .hatvalues >= 0.99        
#     -38,167,642 < minmax(.rstudent) < 49,649,823    
#     , <comma-separated-<glbFeatsId>>
#                                     )
glbObsTrnOutliers <- list()
glbObsTrnOutliers[["Final"]] <- union(glbObsFitOutliers[["All.X"]],
                                c(NULL
                                ))

# influence.measures: car::outlier; rstudent; dffits; hatvalues; dfbeta; dfbetas
#mdlId <- "All.X##rcv#glm"; obs_df <- fitobs_df
#mdlId <- "RFE.X.glm"; obs_df <- fitobs_df
#mdlId <- "Final.glm"; obs_df <- trnobs_df
#mdlId <- "CSM2.X.glm"; obs_df <- fitobs_df
#print(outliers <- car::outlierTest(glb_models_lst[[mdlId]]$finalModel))
#mdlIdFamily <- paste0(head(unlist(str_split(mdlId, "\\.")), -1), collapse="."); obs_df <- dplyr::filter_(obs_df, interp(~(!(var %in% glbObsFitOutliers[[mdlIdFamily]])), var = as.name(glbFeatsId))); model_diags_df <- cbind(obs_df, data.frame(.rstudent=stats::rstudent(glb_models_lst[[mdlId]]$finalModel)), data.frame(.dffits=stats::dffits(glb_models_lst[[mdlId]]$finalModel)), data.frame(.hatvalues=stats::hatvalues(glb_models_lst[[mdlId]]$finalModel)));print(summary(model_diags_df[, c(".rstudent",".dffits",".hatvalues")])); table(cut(model_diags_df$.hatvalues, breaks=c(0.00, 0.98, 0.99, 1.00)))

#print(subset(model_diags_df, is.na(.rstudent))[, glbFeatsId])
#print(model_diags_df[which.max(model_diags_df$.rstudent), ])
#print(subset(model_diags_df, is.na(.dffits))[, glbFeatsId])
#print(model_diags_df[which.min(model_diags_df$.dffits), ])
#print(subset(model_diags_df, .hatvalues > 0.99)[, glbFeatsId])
#dffits_df <- merge(dffits_df, outliers_df, by="row.names", all.x=TRUE); row.names(dffits_df) <- dffits_df$Row.names; dffits_df <- subset(dffits_df, select=-Row.names)
#dffits_df <- merge(dffits_df, glbObsFit, by="row.names", all.x=TRUE); row.names(dffits_df) <- dffits_df$Row.names; dffits_df <- subset(dffits_df, select=-Row.names)
#subset(dffits_df, !is.na(.Bonf.p))

#mdlId <- "CSM.X.glm"; vars <- myextract_actual_feats(row.names(orderBy(reformulate(c("-", paste0(mdlId, ".imp"))), myget_feats_imp(glb_models_lst[[mdlId]])))); 
#model_diags_df <- glb_get_predictions(model_diags_df, mdlId, glb_rsp_var)
#obs_ix <- row.names(model_diags_df) %in% names(outliers$rstudent)[1]
#obs_ix <- which(is.na(model_diags_df$.rstudent))
#obs_ix <- which(is.na(model_diags_df$.dffits))
#myplot_parcoord(obs_df=model_diags_df[, c(glbFeatsId, glbFeatsCategory, ".rstudent", ".dffits", ".hatvalues", glb_rsp_var, paste0(glb_rsp_var, mdlId), vars[1:min(20, length(vars))])], obs_ix=obs_ix, id_var=glbFeatsId, category_var=glbFeatsCategory)

#model_diags_df[row.names(model_diags_df) %in% names(outliers$rstudent)[c(1:2)], ]
#ctgry_diags_df <- model_diags_df[model_diags_df[, glbFeatsCategory] %in% c("Unknown#0"), ]
#myplot_parcoord(obs_df=ctgry_diags_df[, c(glbFeatsId, glbFeatsCategory, ".rstudent", ".dffits", ".hatvalues", glb_rsp_var, "startprice.log10.predict.RFE.X.glmnet", indepVar[1:20])], obs_ix=row.names(ctgry_diags_df) %in% names(outliers$rstudent)[1], id_var=glbFeatsId, category_var=glbFeatsCategory)
#table(glbObsFit[model_diags_df[, glbFeatsCategory] %in% c("iPad1#1"), "startprice.log10.cut.fctr"])
#glbObsFit[model_diags_df[, glbFeatsCategory] %in% c("iPad1#1"), c(glbFeatsId, "startprice")]

# No outliers & .dffits == NaN
#myplot_parcoord(obs_df=model_diags_df[, c(glbFeatsId, glbFeatsCategory, glb_rsp_var, "startprice.log10.predict.RFE.X.glmnet", indepVar[1:10])], obs_ix=seq(1:nrow(model_diags_df))[is.na(model_diags_df$.dffits)], id_var=glbFeatsId, category_var=glbFeatsCategory)

# Modify mdlId to (build & extract) "<FamilyId>#<Fit|Trn>#<caretMethod>#<preProc1.preProc2>#<samplingMethod>"
glb_models_lst <- list(); glb_models_df <- data.frame()

# Add xgboost algorithm

# Regression
if (glb_is_regression) {
    glbMdlMethods <- c(NULL
        # deterministic
            #, "lm", # same as glm
            , "glm", "bayesglm", "glmnet"
            , "rpart"
        # non-deterministic
            , "gbm", "rf" 
        # Unknown
            , "nnet" , "avNNet" # runs 25 models per cv sample for tunelength=5
            , "svmLinear", "svmLinear2"
            , "svmPoly" # runs 75 models per cv sample for tunelength=5
            , "svmRadial" 
            , "earth"
            , "bagEarth" # Takes a long time
            ,"xgbLinear","xgbTree"
        )
} else
# Classification - Add ada (auto feature selection)
    if (glb_is_binomial)
        glbMdlMethods <- c(NULL
        # deterministic                     
            , "bagEarth" # Takes a long time        
            , "glm", "bayesglm", "glmnet"
            , "nnet"
            , "rpart"
        # non-deterministic        
            , "gbm"
            , "avNNet" # runs 25 models per cv sample for tunelength=5      
            , "rf"
        # Unknown
            , "lda", "lda2"
                # svm models crash when predict is called -> internal to kernlab it should call predict without .outcome
            , "svmLinear", "svmLinear2"
            , "svmPoly" # runs 75 models per cv sample for tunelength=5
            , "svmRadial" 
            , "earth"
            ,"xgbLinear","xgbTree"
        ) else
        glbMdlMethods <- c(NULL
        # deterministic
            ,"glmnet"
        # non-deterministic 
            ,"rf"       
        # Unknown
            ,"gbm","rpart","xgbLinear","xgbTree"
        )

glbMdlFamilies <- list(); glb_mdl_feats_lst <- list()
# family: Choose from c("RFE.X", "CSM.X", "All.X", "Best.Interact")
#   methods: Choose from c(NULL, <method>, glbMdlMethods) 
#glbMdlFamilies[["RFE.X"]] <- c("glmnet", "glm") # non-NULL vector is mandatory
if (glb_is_classification && !glb_is_binomial)
    # glm does not work for multinomial
    glbMdlFamilies[["All.X"]] <- c("glmnet") else    
    glbMdlFamilies[["All.X"]] <- c("glmnet", "glm")

#glbMdlFamilies[["Best.Interact"]] <- "glmnet" # non-NULL vector is mandatory

# Check if interaction features make RFE better
# glbMdlFamilies[["CSM.X"]] <- setdiff(glbMdlMethods, c("lda", "lda2")) # crashing due to category:.clusterid ??? #c("glmnet", "glm") # non-NULL list is mandatory
# glb_mdl_feats_lst[["CSM.X"]] <- c(NULL
#     , <comma-separated-features-vector>
#                                   )
# dAFeats.CSM.X %<d-% c(NULL
#     # Interaction feats up to varImp(RFE.X.glmnet) >= 50
#     , <comma-separated-features-vector>
#     , setdiff(myextract_actual_feats(predictors(rfe_fit_results)), c(NULL
#                , <comma-separated-features-vector>
#                                                                       ))    
#                                   )
# glb_mdl_feats_lst[["CSM.X"]] <- "%<d-% dAFeats.CSM.X"

glbMdlFamilies[["Final"]] <- c(NULL) # NULL vector acceptable # c("glmnet", "glm")

glbMdlAllowParallel <- list()
#glbMdlAllowParallel[["Final##rcv#glmnet"]] <- FALSE
glbMdlAllowParallel[["All.X##rcv#glm"]] <- FALSE

# Check if tuning parameters make fit better; make it mdlFamily customizable ?
glbMdlTuneParams <- data.frame()
# When glmnet crashes at model$grid with error: ???
glmnetTuneParams <- rbind(data.frame()
                        ,data.frame(parameter = "alpha",  vals = "0.100 0.325 0.550 0.775 1.000")
                        ,data.frame(parameter = "lambda", vals = "9.342e-02")    
                        )
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams,
#                                cbind(data.frame(mdlId = "<mdlId>"),
#                                      glmnetTuneParams))

    #avNNet    
    #   size=[1] 3 5 7 9; decay=[0] 1e-04 0.001  0.01   0.1; bag=[FALSE]; RMSE=1.3300906 

    #bagEarth
    #   degree=1 [2] 3; nprune=64 128 256 512 [1024]; RMSE=0.6486663 (up)
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
#     ,data.frame(method = "bagEarth", parameter = "nprune", vals = "256")
#     ,data.frame(method = "bagEarth", parameter = "degree", vals = "2")    
# ))

    #earth 
    #   degree=[1]; nprune=2  [9] 17 25 33; RMSE=0.1334478
    
    #gbm 
    #   shrinkage=0.05 [0.10] 0.15 0.20 0.25; n.trees=100 150 200 [250] 300; interaction.depth=[1] 2 3 4 5; n.minobsinnode=[10]; RMSE=0.2008313     
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
#     ,data.frame(method = "gbm", parameter = "shrinkage", min = 0.05, max = 0.25, by = 0.05)
#     ,data.frame(method = "gbm", parameter = "n.trees", min = 100, max = 300, by = 50)
#     ,data.frame(method = "gbm", parameter = "interaction.depth", min = 1, max = 5, by = 1)
#     ,data.frame(method = "gbm", parameter = "n.minobsinnode", min = 10, max = 10, by = 10)
#     #seq(from=0.05,  to=0.25, by=0.05)
# ))

    #glmnet
    #   alpha=0.100 [0.325] 0.550 0.775 1.000; lambda=0.0005232693 0.0024288010 0.0112734954 [0.0523269304] 0.2428800957; RMSE=0.6164891
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
#     ,data.frame(method = "glmnet", parameter = "alpha", vals = "0.550 0.775 0.8875 0.94375 1.000")
#     ,data.frame(method = "glmnet", parameter = "lambda", vals = "9.858855e-05 0.0001971771 0.0009152152 0.0042480525 0.0197177130")    
# ))

    #nnet    
    #   size=3 5 [7] 9 11; decay=0.0001 0.001 0.01 [0.1] 0.2; RMSE=0.9287422
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
#     ,data.frame(method = "nnet", parameter = "size", vals = "3 5 7 9 11")
#     ,data.frame(method = "nnet", parameter = "decay", vals = "0.0001 0.0010 0.0100 0.1000 0.2000")    
# ))

    #rf # Don't bother; results are not deterministic
    #       mtry=2  35  68 [101] 134; RMSE=0.1339974
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
#     ,data.frame(method = "rf", parameter = "mtry", vals = "2 5 9 13 17")
# ))

    #rpart 
    #   cp=0.020 [0.025] 0.030 0.035 0.040; RMSE=0.1770237
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()    
#     ,data.frame(method = "rpart", parameter = "cp", vals = "0.004347826 0.008695652 0.017391304 0.021739130 0.034782609")
# ))
    
    #svmLinear
    #   C=0.01 0.05 [0.10] 0.50 1.00 2.00 3.00 4.00; RMSE=0.1271318; 0.1296718
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
#     ,data.frame(method = "svmLinear", parameter = "C", vals = "0.01 0.05 0.1 0.5 1")
# ))

    #svmLinear2    
    #   cost=0.0625 0.1250 [0.25] 0.50 1.00; RMSE=0.1276354 
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
#     ,data.frame(method = "svmLinear2", parameter = "cost", vals = "0.0625 0.125 0.25 0.5 1")
# ))

    #svmPoly    
    #   degree=[1] 2 3 4 5; scale=0.01 0.05 [0.1] 0.5 1; C=0.50 1.00 [2.00] 3.00 4.00; RMSE=0.1276130
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
#     ,data.frame(method="svmPoly", parameter="degree", min=1, max=5, by=1) #seq(1, 5, 1)
#     ,data.frame(method="svmPoly", parameter="scale", vals="0.01, 0.05, 0.1, 0.5, 1")
#     ,data.frame(method="svmPoly", parameter="C", vals="0.50, 1.00, 2.00, 3.00, 4.00")    
# ))

    #svmRadial
    #   sigma=[0.08674323]; C=0.25 0.50 1.00 [2.00] 4.00; RMSE=0.1614957
    
#glb2Sav(); all.equal(sav_models_df, glb_models_df)
    
glb_preproc_methods <- NULL
#     c("YeoJohnson", "center.scale", "range", "pca", "ica", "spatialSign")

# Baseline prediction model feature(s)
glb_Baseline_mdl_var <- NULL # or c("<feat>")

glbMdlMetric_terms <- NULL # or matrix(c(
#                               0,1,2,3,4,
#                               2,0,1,2,3,
#                               4,2,0,1,2,
#                               6,4,2,0,1,
#                               8,6,4,2,0
#                           ), byrow=TRUE, nrow=5)
glbMdlMetricSummary <- NULL # or "<metric_name>"
glbMdlMetricMaximize <- NULL # or FALSE (TRUE is not the default for both classification & regression) 
glbMdlMetricSummaryFn <- NULL # or function(data, lev=NULL, model=NULL) {
#     confusion_mtrx <- t(as.matrix(confusionMatrix(data$pred, data$obs)))
#     #print(confusion_mtrx)
#     #print(confusion_mtrx * glbMdlMetric_terms)
#     metric <- sum(confusion_mtrx * glbMdlMetric_terms) / nrow(data)
#     names(metric) <- glbMdlMetricSummary
#     return(metric)
# }

glbMdlCheckRcv <- FALSE # Turn it on when needed; otherwise takes long time
glb_rcv_n_folds <- 3 # or NULL
glb_rcv_n_repeats <- 3 # or NULL

glb_clf_proba_threshold <- NULL # 0.5

# Model selection criteria
if (glb_is_regression)
    glbMdlMetricsEval <- c("min.RMSE.OOB", "max.R.sq.OOB", "max.Adj.R.sq.fit", "min.RMSE.fit")
    #glbMdlMetricsEval <- c("min.RMSE.fit", "max.R.sq.fit", "max.Adj.R.sq.fit")    
if (glb_is_classification) {
    if (glb_is_binomial)
        glbMdlMetricsEval <- 
            c("max.Accuracy.OOB", "max.AUCROCR.OOB", "max.AUCpROC.OOB", "min.aic.fit", "max.Accuracy.fit") else        
        glbMdlMetricsEval <- c("max.Accuracy.OOB", "max.Kappa.OOB")
}

# select from NULL [no ensemble models], "auto" [all models better than MFO or Baseline], c(mdl_ids in glb_models_lst) [Typically top-rated models in auto]
glb_mdl_ensemble <- NULL
#     "%<d-% setdiff(mygetEnsembleAutoMdlIds(), 'CSM.X.rf')" 
#     c(<comma-separated-mdlIds>
#      )

# Only for classifications; for regressions remove "(.*)\\.prob" form the regex
# tmp_fitobs_df <- glbObsFit[, grep(paste0("^", gsub(".", "\\.", mygetPredictIds$value, fixed = TRUE), "CSM\\.X\\.(.*)\\.prob"), names(glbObsFit), value = TRUE)]; cor_mtrx <- cor(tmp_fitobs_df); cor_vctr <- sort(cor_mtrx[row.names(orderBy(~-Overall, varImp(glb_models_lst[["Ensemble.repeatedcv.glmnet"]])$imp))[1], ]); summary(cor_vctr); cor_vctr
#ntv.glm <- glm(reformulate(indepVar, glb_rsp_var), family = "binomial", data = glbObsFit)
#step.glm <- step(ntv.glm)

glbMdlSelId <- "All.X##rcv#glmnet" #select from c(NULL, "All.X##rcv#glmnet", "RFE.X##rcv#glmnet", <mdlId>)
glbMdlFinId <- NULL #select from c(NULL, glbMdlSelId)

glb_dsp_cols <- c(".pos", glbFeatsId, glbFeatsCategory, glb_rsp_var
#               List critical cols excl. above
                  )

# Output specs
# lclgetfltout_df <- function(obsOutFinDf) {
#     require(tidyr)
#     obsOutFinDf <- obsOutFinDf %>%
#         tidyr::separate("ImageId.x.y", c(".src", ".pos", "x", "y"), 
#                         sep = "#", remove = TRUE, extra = "merge")
#     # mnm prefix stands for max_n_mean
#     mnmout_df <- obsOutFinDf %>%
#         dplyr::group_by(.pos) %>%
#         #dplyr::top_n(1, Probability1) %>% # Score = 3.9426         
#         #dplyr::top_n(2, Probability1) %>% # Score = ???; weighted = 3.94254;         
#         #dplyr::top_n(3, Probability1) %>% # Score = 3.9418; weighted = 3.94169; 
#         dplyr::top_n(4, Probability1) %>% # Score = ???; weighted = 3.94149;        
#         #dplyr::top_n(5, Probability1) %>% # Score = 3.9421; weighted = 3.94178
#     
#         # dplyr::summarize(xMeanN = mean(as.numeric(x)), yMeanN = mean(as.numeric(y)))
#         # dplyr::summarize(xMeanN = weighted.mean(as.numeric(x), Probability1), yMeanN = mean(as.numeric(y)))
#         # dplyr::summarize(xMeanN = weighted.mean(as.numeric(x), c(Probability1, 0.2357323, 0.2336925)), yMeanN = mean(as.numeric(y)))    
#         # dplyr::summarize(xMeanN = weighted.mean(as.numeric(x), c(Probability1)), yMeanN = mean(as.numeric(y)))
#         dplyr::summarize(xMeanN = weighted.mean(as.numeric(x), c(Probability1)), 
#                          yMeanN = weighted.mean(as.numeric(y), c(Probability1)))  
#     
#     maxout_df <- obsOutFinDf %>%
#         dplyr::group_by(.pos) %>%
#         dplyr::summarize(maxProb1 = max(Probability1))
#     fltout_df <- merge(maxout_df, obsOutFinDf, 
#                        by.x = c(".pos", "maxProb1"), by.y = c(".pos", "Probability1"),
#                        all.x = TRUE)
#     fmnout_df <- merge(fltout_df, mnmout_df, 
#                        by.x = c(".pos"), by.y = c(".pos"),
#                        all.x = TRUE)
#     return(fmnout_df)
# }
glbObsOut <- list(NULL
        # glbFeatsId will be the first output column, by default
        ,vars = list()
#         ,mapFn = function(obsOutFinDf) {
#                   }
                  )
#obsOutFinDf <- savobsOutFinDf
# glbObsOut$mapFn <- function(obsOutFinDf) {
#     txfout_df <- dplyr::select(obsOutFinDf, -.pos.y) %>%
#         dplyr::mutate(
#             lunch     = levels(glbObsTrn[, "lunch"    ])[
#                        round(mean(as.numeric(glbObsTrn[, "lunch"    ])), 0)],
#             dinner    = levels(glbObsTrn[, "dinner"   ])[
#                        round(mean(as.numeric(glbObsTrn[, "dinner"   ])), 0)],
#             reserve   = levels(glbObsTrn[, "reserve"  ])[
#                        round(mean(as.numeric(glbObsTrn[, "reserve"  ])), 0)],
#             outdoor   = levels(glbObsTrn[, "outdoor"  ])[
#                        round(mean(as.numeric(glbObsTrn[, "outdoor"  ])), 0)],
#             expensive = levels(glbObsTrn[, "expensive"])[
#                        round(mean(as.numeric(glbObsTrn[, "expensive"])), 0)],
#             liquor    = levels(glbObsTrn[, "liquor"   ])[
#                        round(mean(as.numeric(glbObsTrn[, "liquor"   ])), 0)],
#             table     = levels(glbObsTrn[, "table"    ])[
#                        round(mean(as.numeric(glbObsTrn[, "table"    ])), 0)],
#             classy    = levels(glbObsTrn[, "classy"   ])[
#                        round(mean(as.numeric(glbObsTrn[, "classy"   ])), 0)],
#             kids      = levels(glbObsTrn[, "kids"     ])[
#                        round(mean(as.numeric(glbObsTrn[, "kids"     ])), 0)]
#                       )
#     
#     print("ObsNew output class tables:")
#     print(sapply(c("lunch","dinner","reserve","outdoor",
#                    "expensive","liquor","table",
#                    "classy","kids"), 
#                  function(feat) table(txfout_df[, feat], useNA = "ifany")))
#     
#     txfout_df <- txfout_df %>%
#         dplyr::mutate(labels = "") %>%
#         dplyr::mutate(labels = 
#     ifelse(lunch     != "-1", paste(labels, lunch    ), labels)) %>%
#         dplyr::mutate(labels = 
#     ifelse(dinner    != "-1", paste(labels, dinner   ), labels)) %>%
#         dplyr::mutate(labels = 
#     ifelse(reserve   != "-1", paste(labels, reserve  ), labels)) %>%
#         dplyr::mutate(labels = 
#     ifelse(outdoor   != "-1", paste(labels, outdoor  ), labels)) %>%
#         dplyr::mutate(labels =         
#     ifelse(expensive != "-1", paste(labels, expensive), labels)) %>%
#         dplyr::mutate(labels =         
#     ifelse(liquor    != "-1", paste(labels, liquor   ), labels)) %>%
#         dplyr::mutate(labels =         
#     ifelse(table     != "-1", paste(labels, table    ), labels)) %>%
#         dplyr::mutate(labels =         
#     ifelse(classy    != "-1", paste(labels, classy   ), labels)) %>%
#         dplyr::mutate(labels =         
#     ifelse(kids      != "-1", paste(labels, kids     ), labels)) %>%
#         dplyr::select(business_id, labels)
#     return(txfout_df)
# }
#if (!is.null(glbObsOut$mapFn)) obsOutFinDf <- glbObsOut$mapFn(obsOutFinDf); print(head(obsOutFinDf))

glb_out_obs <- NULL # select from c(NULL : default to "new", "all", "new", "trn")

if (glb_is_classification && glb_is_binomial) {
    # glbObsOut$vars[["Probability1"]] <- 
    #     "%<d-% glbObsNew[, mygetPredictIds(glb_rsp_var, glbMdlFinId)$prob]" 
    # glbObsOut$vars[[glb_rsp_var_raw]] <-
    #     "%<d-% glb_map_rsp_var_to_raw(glbObsNew[,
    #                                         mygetPredictIds(glb_rsp_var, glbMdlFinId)$value])"
    glbObsOut$vars[["Predictions"]] <-
        "%<d-% glb_map_rsp_var_to_raw(glbObsNew[,
                                            mygetPredictIds(glb_rsp_var, glbMdlFinId)$value])"
} else {
#     glbObsOut$vars[[glbFeatsId]] <- 
#         "%<d-% as.integer(gsub('Test#', '', glbObsNew[, glbFeatsId]))"
    glbObsOut$vars[[glb_rsp_var]] <- 
        "%<d-% glbObsNew[, mygetPredictIds(glb_rsp_var, glbMdlFinId)$value]"
#     for (outVar in setdiff(glbFeatsExcludeLcl, glb_rsp_var_raw))
#         glbObsOut$vars[[outVar]] <- 
#             paste0("%<d-% mean(glbObsAll[, \"", outVar, "\"], na.rm = TRUE)")
}    
# glbObsOut$vars[[glb_rsp_var_raw]] <- glb_rsp_var_raw
# glbObsOut$vars[[paste0(head(unlist(strsplit(mygetPredictIds$value, "")), -1), collapse = "")]] <-

glbOutStackFnames <- NULL #: default
    # c("ebayipads_txt_assoc1_out_bid1_stack.csv") # manual stack
    # c("ebayipads_finmdl_bid1_out_nnet_1.csv") # universal stack

glbOut <- list(pfx = "Votes_Q_02_cluster_")
# lclImageSampleSeed <- 129
glbOutDataVizFname <- NULL # choose from c(NULL, "<projectId>_obsall.csv")


glbChunks <- list(labels = c("set_global_options_wd","set_global_options"
    ,"import.data","inspect.data","scrub.data","transform.data"
    ,"extract.features"
        ,"extract.features.datetime","extract.features.image","extract.features.price"
        ,"extract.features.text","extract.features.string"  
        ,"extract.features.end"
    ,"manage.missing.data","cluster.data","partition.data.training","select.features"
    ,"fit.models_0","fit.models_1","fit.models_2","fit.models_3"
    ,"fit.data.training_0","fit.data.training_1"
    ,"predict.data.new"         
    ,"display.session.info"))
# To ensure that all chunks in this script are in glbChunks
if (!is.null(chkChunksLabels <- knitr::all_labels()) && # knitr::all_labels() doesn't work in console runs
    !identical(chkChunksLabels, glbChunks$labels)) {
    print(sprintf("setdiff(chkChunksLabels, glbChunks$labels): %s", 
                  setdiff(chkChunksLabels, glbChunks$labels)))    
    print(sprintf("setdiff(glbChunks$labels, chkChunksLabels): %s", 
                  setdiff(glbChunks$labels, chkChunksLabels)))    
}

glbChunks[["first"]] <- "cluster.data" #default: script will load envir from previous chunk
glbChunks[["last"]] <- NULL #NULL #default: script will save envir at end of this chunk 
glbChunks[["inpFilePathName"]] <- "data/Votes_Q_01_cnk_manage.missing.data.RData"
#mysavChunk(glbOut$pfx, glbChunks[["last"]]) # called from myevlChunk

# Inspect max OOB FP
#chkObsOOB <- subset(glbObsOOB, !label.fctr.All.X..rcv.glmnet.is.acc)
#chkObsOOBFP <- subset(chkObsOOB, label.fctr.All.X..rcv.glmnet == "left_eye_center") %>% dplyr::mutate(Probability1 = label.fctr.All.X..rcv.glmnet.prob) %>% select(-.src, -.pos, -x, -y) %>% lclgetfltout_df() %>% mutate(obj.distance = (((as.numeric(x) - left_eye_center_x.int) ^ 2) + ((as.numeric(y) - left_eye_center_y.int) ^ 2)) ^ 0.5) %>% dplyr::top_n(5, obj.distance) %>% dplyr::top_n(5, -patch.cor)
#
#newImgObs <- glbObsNew[(glbObsNew$ImageId == "Test#0001"), ]; print(newImgObs[which.max(newImgObs$label.fctr.Final..rcv.glmnet.prob), ])
#OOBImgObs <- glbObsOOB[(glbObsOOB$ImageId == "Train#0003"), ]; print(OOBImgObs[which.max(OOBImgObs$label.fctr.All.X..rcv.glmnet.prob), ])

#load("Votes_Q_02_cnk_extract.features.end.RData", verbose = TRUE)
#mygetImage(which(glbObsAll[, glbFeatsId] == "Train#0003"), names(glbFeatsImage)[1], plot = TRUE, featHighlight = c("left_eye_center_x", "left_eye_center_y"), ovrlHighlight = c(66, 35))

# Depict process
glb_analytics_pn <- petrinet(name = "glb_analytics_pn",
                        trans_df = data.frame(id = 1:6,
    name = c("data.training.all","data.new",
           "model.selected","model.final",
           "data.training.all.prediction","data.new.prediction"),
    x=c(   -5,-5,-15,-25,-25,-35),
    y=c(   -5, 5,  0,  0, -5,  5)
                        ),
                        places_df=data.frame(id=1:4,
    name=c("bgn","fit.data.training.all","predict.data.new","end"),
    x=c(   -0,   -20,                    -30,               -40),
    y=c(    0,     0,                      0,                 0),
    M0=c(   3,     0,                      0,                 0)
                        ),
                        arcs_df = data.frame(
    begin = c("bgn","bgn","bgn",        
            "data.training.all","model.selected","fit.data.training.all",
            "fit.data.training.all","model.final",    
            "data.new","predict.data.new",
            "data.training.all.prediction","data.new.prediction"),
    end   = c("data.training.all","data.new","model.selected",
            "fit.data.training.all","fit.data.training.all","model.final",
            "data.training.all.prediction","predict.data.new",
            "predict.data.new","data.new.prediction",
            "end","end")
                        ))
#print(ggplot.petrinet(glb_analytics_pn))
print(ggplot.petrinet(glb_analytics_pn) + coord_flip())

## Loading required package: grid

glb_analytics_avl_objs <- NULL

glb_chunks_df <- myadd_chunk(NULL, 
                             ifelse(is.null(glbChunks$first), "import.data", glbChunks$first))

##          label step_major step_minor label_minor   bgn end elapsed
## 1 cluster.data          1          0           0 9.593  NA      NA

Step 1.0: cluster data

chunk option: eval=

Step 1.0: cluster data

Step 1.0: cluster data

Step 1.0: cluster data

Step 1.0: cluster data

Step 1.0: cluster data

Step 1.0: cluster data

```{r extract.features.image, cache=FALSE, echo=FALSE, fig.height=5, fig.width=5, eval=myevlChunk(glbChunks, glbOut$pfx)}

Step 1.0: cluster data

Step 1.0: cluster data

Step 1.0: cluster data

Step 1.0: cluster data

Step 1.0: cluster data

Step 1.0: cluster data

{r cluster.data, cache=FALSE, echo=FALSE, eval=myevlChunk(glbChunks, glbOut$pfx)} #{r cluster.data, cache=FALSE, echo=FALSE, eval=myevlChunk(glbChunks, glbOut$pfx, keep = c(“glbFeatsCategory”,“glb_dsp_cols”))}

## Loading required package: proxy

## 
## Attaching package: 'proxy'

## The following objects are masked from 'package:stats':
## 
##     as.dist, dist

## The following object is masked from 'package:base':
## 
##     as.matrix

## Loading required package: dynamicTreeCut

## Loading required package: entropy

## Loading required package: tidyr

## Loading required package: ggdendro

## [1] "Clustering features: "
##               abs.cor.y
## Q120472.fctr 0.04620307
## Q98197.fctr  0.05493425
## Q113181.fctr 0.08087531
## Q115611.fctr 0.09044682
## Q109244.fctr 0.12038125
## [1] "    .rnorm cor: -0.0078"
## [1] "  Clustering entropy measure: Party.fctr"
## [1] "glbObsAll Entropy: 0.6913"

## Loading required package: lazyeval

##   Hhold.fctr .clusterid Hhold.fctr.clusterid    R    D  .entropy .knt
## 1          N          1                  N_1  220  230 0.6929002  450
## 2        MKn          1                MKn_1  308  344 0.6916221  652
## 3        MKy          1                MKy_1  842  752 0.6915524 1594
## 4        PKn          1                PKn_1   49  131 0.5854566  180
## 5        PKy          1                PKy_1   26   35 0.6822232   61
## 6        SKn          1                SKn_1 1091 1340 0.6878923 2431
## 7        SKy          1                SKy_1   81  119 0.6749870  200
## [1] "glbObsAll$Hhold.fctr Entropy: 0.6859 (99.2186 pct)"

## [1] "Category: N"
## [1] "max distance(0.9785) pair:"
##      USER_ID Party.fctr Hhold.fctr Q124742.fctr Q124122.fctr Q123621.fctr
## 4459    5563          D          N           NA           NA           NA
## 5038    6295          R          N           No           No           No
##      Q123464.fctr Q122771.fctr Q122770.fctr Q122769.fctr Q122120.fctr
## 4459           NA           NA           NA           NA           NA
## 5038           No           Pc          Yes          Yes          Yes
##      Q121700.fctr Q121699.fctr Q121011.fctr Q120978.fctr Q120650.fctr
## 4459           NA           NA           NA           NA           NA
## 5038           No           No           No           No          Yes
##      Q120472.fctr Q120379.fctr Q120194.fctr Q120014.fctr Q120012.fctr
## 4459           NA           NA           NA           NA           NA
## 5038      Science          Yes    Try first          Yes           No
##      Q119851.fctr Q119650.fctr Q119334.fctr Q118892.fctr Q118237.fctr
## 4459          Yes       Giving           No          Yes           No
## 5038           No       Giving           No           No           No
##      Q118233.fctr Q118232.fctr Q118117.fctr   Q117193.fctr Q117186.fctr
## 4459          Yes           Pr           No Standard hours   Hot headed
## 5038           NA           NA          Yes Standard hours  Cool headed
##      Q116797.fctr Q116881.fctr Q116953.fctr Q116601.fctr Q116441.fctr
## 4459           NA           NA           NA           NA           NA
## 5038           NA           NA           NA           NA           NA
##      Q116448.fctr Q116197.fctr Q115602.fctr Q115777.fctr Q115610.fctr
## 4459           NA           NA           NA           NA           NA
## 5038           NA           NA           NA           NA           NA
##      Q115611.fctr Q115899.fctr Q115390.fctr Q115195.fctr Q114961.fctr
## 4459           NA           NA           NA           NA           NA
## 5038           NA           NA           NA           No          Yes
##      Q114748.fctr Q114517.fctr Q114386.fctr Q114152.fctr Q113992.fctr
## 4459           NA           NA           NA           NA           NA
## 5038          Yes           No   Mysterious          Yes           No
##      Q113583.fctr Q113584.fctr Q113181.fctr Q112478.fctr Q112512.fctr
## 4459           NA           NA           NA          Yes           NA
## 5038        Tunes       People           NA           No           NA
##      Q112270.fctr Q111848.fctr Q111580.fctr Q111220.fctr Q110740.fctr
## 4459          Yes           No   Supportive           No          Mac
## 5038           No          Yes           NA           NA          Mac
##      Q109367.fctr Q109244.fctr  Q108950.fctr Q108855.fctr Q108617.fctr
## 4459           NA           NA Risk-friendly         Yes!           No
## 5038           NA           NA            NA           NA           NA
##      Q108856.fctr Q108754.fctr Q108342.fctr Q108343.fctr Q107869.fctr
## 4459        Space           No    In-person           No           No
## 5038           NA           NA           NA           NA           NA
##      Q107491.fctr Q106993.fctr Q106997.fctr Q106272.fctr Q106388.fctr
## 4459          Yes          Yes           Gr          Yes           No
## 5038           NA           NA           NA           NA           NA
##      Q106389.fctr Q106042.fctr Q105840.fctr Q105655.fctr Q104996.fctr
## 4459          Yes           No           No          Yes           No
## 5038           NA           NA           NA           NA           NA
##      Q103293.fctr Q102906.fctr Q102674.fctr Q102687.fctr Q102289.fctr
## 4459          Yes          Yes           No          Yes           No
## 5038           NA           NA           NA           NA           NA
##      Q102089.fctr Q101162.fctr Q101163.fctr Q101596.fctr Q100689.fctr
## 4459         Rent     Optimist          Mom           No          Yes
## 5038           NA           NA           NA           NA           NA
##      Q100680.fctr Q100562.fctr Q100010.fctr Q99982.fctr Q99716.fctr
## 4459           No           No          Yes        Nope          No
## 5038           NA           NA           NA          NA          NA
##      Q99581.fctr Q99480.fctr Q98869.fctr Q98578.fctr Q98197.fctr
## 4459          No          No         Yes          No          No
## 5038          NA          NA          NA          NA          NA
##      Q98059.fctr Q98078.fctr Q96024.fctr
## 4459         Yes         Yes         Yes
## 5038          NA          NA          NA
## [1] "min distance(0.9441) pair:"
##      USER_ID Party.fctr Hhold.fctr Q124742.fctr Q124122.fctr Q123621.fctr
## 4930    6157          D          N          Yes           NA           NA
## 6569    5059       <NA>          N           NA           NA           NA
##      Q123464.fctr Q122771.fctr Q122770.fctr Q122769.fctr Q122120.fctr
## 4930           NA           NA           NA           NA           NA
## 6569           NA           Pc           No           NA           NA
##      Q121700.fctr Q121699.fctr Q121011.fctr Q120978.fctr Q120650.fctr
## 4930           NA           NA           NA           NA           NA
## 6569           NA           NA           NA           NA           NA
##      Q120472.fctr Q120379.fctr Q120194.fctr Q120014.fctr Q120012.fctr
## 4930           NA           NA           NA           NA           NA
## 6569           NA           NA           NA           NA           NA
##      Q119851.fctr Q119650.fctr Q119334.fctr Q118892.fctr Q118237.fctr
## 4930           NA           NA           NA           NA           NA
## 6569           NA           NA           NA           NA           NA
##      Q118233.fctr Q118232.fctr Q118117.fctr Q117193.fctr Q117186.fctr
## 4930           NA           NA           NA           NA           NA
## 6569           NA           NA           NA           NA           NA
##      Q116797.fctr Q116881.fctr Q116953.fctr Q116601.fctr Q116441.fctr
## 4930           NA           NA           NA           NA           NA
## 6569           NA           NA           NA           NA           NA
##      Q116448.fctr Q116197.fctr Q115602.fctr Q115777.fctr Q115610.fctr
## 4930           NA           NA           NA           NA           NA
## 6569           NA           NA           NA           NA           NA
##      Q115611.fctr Q115899.fctr Q115390.fctr Q115195.fctr Q114961.fctr
## 4930           NA           NA           NA           NA          Yes
## 6569           NA           NA           NA           NA           NA
##      Q114748.fctr Q114517.fctr Q114386.fctr Q114152.fctr Q113992.fctr
## 4930           No           No   Mysterious           No           No
## 6569           NA           NA           NA           NA           NA
##      Q113583.fctr Q113584.fctr Q113181.fctr Q112478.fctr Q112512.fctr
## 4930        Tunes   Technology           NA           NA           NA
## 6569        Tunes   Technology           No           No          Yes
##      Q112270.fctr Q111848.fctr Q111580.fctr Q111220.fctr Q110740.fctr
## 4930           NA           NA           NA           NA           NA
## 6569           No           NA           NA           No           NA
##      Q109367.fctr Q109244.fctr Q108950.fctr Q108855.fctr Q108617.fctr
## 4930          Yes          Yes     Cautious           NA           NA
## 6569           No          Yes           NA       Umm...          Yes
##      Q108856.fctr Q108754.fctr Q108342.fctr Q108343.fctr Q107869.fctr
## 4930           NA           NA           NA           NA           NA
## 6569        Space           NA           NA           NA           NA
##      Q107491.fctr Q106993.fctr Q106997.fctr Q106272.fctr Q106388.fctr
## 4930           NA           NA           NA           NA           NA
## 6569           NA           NA           NA           NA           NA
##      Q106389.fctr Q106042.fctr Q105840.fctr Q105655.fctr Q104996.fctr
## 4930           NA           NA           NA           NA           NA
## 6569           NA           NA           NA           NA           No
##      Q103293.fctr Q102906.fctr Q102674.fctr Q102687.fctr Q102289.fctr
## 4930           NA           NA           NA           NA           NA
## 6569          Yes          Yes           NA           No           NA
##      Q102089.fctr Q101162.fctr Q101163.fctr Q101596.fctr Q100689.fctr
## 4930           NA           NA           NA           NA           NA
## 6569           NA           NA           NA           NA           NA
##      Q100680.fctr Q100562.fctr Q100010.fctr Q99982.fctr Q99716.fctr
## 4930           NA           NA           NA          NA          NA
## 6569           NA           NA           NA          NA          NA
##      Q99581.fctr Q99480.fctr Q98869.fctr Q98578.fctr Q98197.fctr
## 4930          NA          NA          NA          NA          NA
## 6569          NA          NA          NA          NA          NA
##      Q98059.fctr Q98078.fctr Q96024.fctr
## 4930          NA          NA          NA
## 6569          NA          NA          NA
## [1] "Category: MKn"
## [1] "max distance(0.9784) pair:"
##      USER_ID Party.fctr Hhold.fctr Q124742.fctr Q124122.fctr Q123621.fctr
## 3547    4420          D        MKn           NA           NA           NA
## 5337    6664          D        MKn          Yes          Yes          Yes
##      Q123464.fctr Q122771.fctr Q122770.fctr Q122769.fctr Q122120.fctr
## 3547           NA           NA           NA           NA           NA
## 5337           No           Pc          Yes          Yes           NA
##      Q121700.fctr Q121699.fctr Q121011.fctr Q120978.fctr Q120650.fctr
## 3547           NA           NA           NA           NA           NA
## 5337           No          Yes          Yes          Yes          Yes
##      Q120472.fctr Q120379.fctr Q120194.fctr Q120014.fctr Q120012.fctr
## 3547           NA           NA           NA           NA           NA
## 5337      Science          Yes  Study first          Yes           No
##      Q119851.fctr Q119650.fctr Q119334.fctr Q118892.fctr Q118237.fctr
## 3547           NA           NA           NA           NA           NA
## 5337          Yes    Receiving          Yes          Yes           NA
##      Q118233.fctr Q118232.fctr Q118117.fctr Q117193.fctr Q117186.fctr
## 3547           NA           NA           NA           NA           NA
## 5337           NA           Pr           No           NA           NA
##      Q116797.fctr Q116881.fctr Q116953.fctr Q116601.fctr Q116441.fctr
## 3547           NA           NA           NA           NA           NA
## 5337           NA           NA           NA           NA           NA
##      Q116448.fctr Q116197.fctr Q115602.fctr Q115777.fctr Q115610.fctr
## 3547           NA           NA           NA           NA           NA
## 5337           NA           NA           NA           NA           NA
##      Q115611.fctr Q115899.fctr Q115390.fctr Q115195.fctr Q114961.fctr
## 3547           NA           NA          Yes          Yes           No
## 5337           NA           NA           NA           NA           NA
##      Q114748.fctr Q114517.fctr Q114386.fctr Q114152.fctr Q113992.fctr
## 3547          Yes           No          TMI           No           No
## 5337           NA           NA           NA           NA           NA
##      Q113583.fctr Q113584.fctr Q113181.fctr Q112478.fctr Q112512.fctr
## 3547        Tunes       People           NA          Yes          Yes
## 5337           NA           NA           NA           NA           NA
##      Q112270.fctr Q111848.fctr Q111580.fctr Q111220.fctr Q110740.fctr
## 3547           No          Yes    Demanding          Yes           PC
## 5337           NA           NA           NA           NA           NA
##      Q109367.fctr Q109244.fctr Q108950.fctr Q108855.fctr Q108617.fctr
## 3547           No           NA     Cautious       Umm...           No
## 5337           NA           NA           NA           NA           NA
##      Q108856.fctr Q108754.fctr Q108342.fctr Q108343.fctr Q107869.fctr
## 3547        Space           No           NA           NA           NA
## 5337           NA           NA           NA           NA           NA
##      Q107491.fctr Q106993.fctr Q106997.fctr Q106272.fctr Q106388.fctr
## 3547           NA           NA           NA           NA           NA
## 5337           NA           NA           NA           NA           NA
##      Q106389.fctr Q106042.fctr Q105840.fctr Q105655.fctr Q104996.fctr
## 3547           NA           NA           NA           NA           NA
## 5337           NA           NA           NA           NA           NA
##      Q103293.fctr Q102906.fctr Q102674.fctr Q102687.fctr Q102289.fctr
## 3547           No           No           No           No           No
## 5337           NA           NA           NA           NA           NA
##      Q102089.fctr Q101162.fctr Q101163.fctr Q101596.fctr Q100689.fctr
## 3547          Own     Optimist          Mom          Yes          Yes
## 5337           NA           NA           NA           NA           NA
##      Q100680.fctr Q100562.fctr Q100010.fctr Q99982.fctr Q99716.fctr
## 3547          Yes           No          Yes      Check!          No
## 5337           NA           NA           NA          NA          NA
##      Q99581.fctr Q99480.fctr Q98869.fctr Q98578.fctr Q98197.fctr
## 3547          No         Yes         Yes          No          No
## 5337          NA          NA          NA          NA          NA
##      Q98059.fctr Q98078.fctr Q96024.fctr
## 3547         Yes          No         Yes
## 5337          NA          NA          NA
## [1] "min distance(0.9373) pair:"
##      USER_ID Party.fctr Hhold.fctr Q124742.fctr Q124122.fctr Q123621.fctr
## 2019    2509          D        MKn           NA           NA           NA
## 3363    4185          D        MKn           NA           NA           NA
##      Q123464.fctr Q122771.fctr Q122770.fctr Q122769.fctr Q122120.fctr
## 2019           NA           NA           NA           NA           NA
## 3363           NA           NA           NA           NA           NA
##      Q121700.fctr Q121699.fctr Q121011.fctr Q120978.fctr Q120650.fctr
## 2019           NA           NA           NA           NA           NA
## 3363           NA           NA           NA           NA           NA
##      Q120472.fctr Q120379.fctr Q120194.fctr Q120014.fctr Q120012.fctr
## 2019           NA           NA           NA           NA           NA
## 3363           NA           NA           NA           NA           NA
##      Q119851.fctr Q119650.fctr Q119334.fctr Q118892.fctr Q118237.fctr
## 2019           NA           NA           NA           NA           NA
## 3363           NA           NA           NA           NA           NA
##      Q118233.fctr Q118232.fctr Q118117.fctr Q117193.fctr Q117186.fctr
## 2019           NA           NA           NA           NA           NA
## 3363           NA           NA           NA           NA           NA
##      Q116797.fctr Q116881.fctr Q116953.fctr Q116601.fctr Q116441.fctr
## 2019           NA           NA           NA           NA           NA
## 3363           NA           NA           NA           NA           NA
##      Q116448.fctr Q116197.fctr Q115602.fctr Q115777.fctr Q115610.fctr
## 2019           NA           NA           NA           NA           NA
## 3363           NA           NA           NA           NA           NA
##      Q115611.fctr Q115899.fctr Q115390.fctr Q115195.fctr Q114961.fctr
## 2019           NA           NA           NA           NA           NA
## 3363           NA           NA           NA           NA           NA
##      Q114748.fctr Q114517.fctr Q114386.fctr Q114152.fctr Q113992.fctr
## 2019           NA           NA           NA           NA           NA
## 3363           NA           NA           NA           NA           NA
##      Q113583.fctr Q113584.fctr Q113181.fctr Q112478.fctr Q112512.fctr
## 2019           NA           NA           NA           NA           NA
## 3363           NA           NA           NA           NA           NA
##      Q112270.fctr Q111848.fctr Q111580.fctr Q111220.fctr Q110740.fctr
## 2019           NA          Yes    Demanding           No          Mac
## 3363           NA           NA           NA           NA           NA
##      Q109367.fctr Q109244.fctr  Q108950.fctr Q108855.fctr Q108617.fctr
## 2019          Yes          Yes Risk-friendly           NA           NA
## 3363           NA          Yes            NA           NA           NA
##      Q108856.fctr Q108754.fctr Q108342.fctr Q108343.fctr Q107869.fctr
## 2019           NA           NA           NA           NA           NA
## 3363           NA           NA           NA           NA           NA
##      Q107491.fctr Q106993.fctr Q106997.fctr Q106272.fctr Q106388.fctr
## 2019           NA           NA           NA           NA           NA
## 3363           NA           NA           NA           NA           NA
##      Q106389.fctr Q106042.fctr Q105840.fctr Q105655.fctr Q104996.fctr
## 2019           NA           NA           NA           NA           NA
## 3363           NA           NA           NA           NA           NA
##      Q103293.fctr Q102906.fctr Q102674.fctr Q102687.fctr Q102289.fctr
## 2019           NA           NA           NA           NA           NA
## 3363           NA           NA           NA           NA           NA
##      Q102089.fctr Q101162.fctr Q101163.fctr Q101596.fctr Q100689.fctr
## 2019           NA           NA           NA           NA           NA
## 3363           NA           NA           NA           NA           NA
##      Q100680.fctr Q100562.fctr Q100010.fctr Q99982.fctr Q99716.fctr
## 2019           NA           NA           NA          NA          NA
## 3363           NA           NA           NA          NA          NA
##      Q99581.fctr Q99480.fctr Q98869.fctr Q98578.fctr Q98197.fctr
## 2019          NA          NA          NA          NA          NA
## 3363          NA          NA          NA          NA          NA
##      Q98059.fctr Q98078.fctr Q96024.fctr
## 2019          NA          NA          NA
## 3363          NA          NA          NA
## [1] "Category: MKy"
## [1] "max distance(0.9789) pair:"
##      USER_ID Party.fctr Hhold.fctr Q124742.fctr Q124122.fctr Q123621.fctr
## 6014    2335       <NA>        MKy           NA           NA           NA
## 6304    3742       <NA>        MKy           No          Yes          Yes
##      Q123464.fctr Q122771.fctr Q122770.fctr Q122769.fctr Q122120.fctr
## 6014           NA           NA           NA           NA           NA
## 6304           No           Pc          Yes          Yes           No
##      Q121700.fctr Q121699.fctr Q121011.fctr Q120978.fctr Q120650.fctr
## 6014           NA           NA           NA           NA           NA
## 6304           No          Yes          Yes           No          Yes
##      Q120472.fctr Q120379.fctr Q120194.fctr Q120014.fctr Q120012.fctr
## 6014           NA           NA           NA           NA           NA
## 6304      Science          Yes  Study first          Yes          Yes
##      Q119851.fctr Q119650.fctr Q119334.fctr Q118892.fctr Q118237.fctr
## 6014           NA           NA           NA           NA           NA
## 6304           No       Giving           No          Yes           No
##      Q118233.fctr Q118232.fctr Q118117.fctr Q117193.fctr Q117186.fctr
## 6014           NA           NA           NA           NA           NA
## 6304           No           Pr           No    Odd hours           NA
##      Q116797.fctr Q116881.fctr Q116953.fctr Q116601.fctr Q116441.fctr
## 6014           NA           NA           NA           NA           NA
## 6304           No        Happy          Yes          Yes          Yes
##      Q116448.fctr Q116197.fctr Q115602.fctr Q115777.fctr Q115610.fctr
## 6014           NA         P.M.           NA           NA           NA
## 6304           No           NA           NA           NA           NA
##      Q115611.fctr Q115899.fctr Q115390.fctr Q115195.fctr Q114961.fctr
## 6014           NA           NA           NA           NA           NA
## 6304           NA           NA           NA          Yes           NA
##      Q114748.fctr Q114517.fctr Q114386.fctr Q114152.fctr Q113992.fctr
## 6014           NA           NA           NA           NA           NA
## 6304           NA           NA           NA           NA           NA
##      Q113583.fctr Q113584.fctr Q113181.fctr Q112478.fctr Q112512.fctr
## 6014           NA           NA           NA          Yes          Yes
## 6304           NA           NA           NA           NA           NA
##      Q112270.fctr Q111848.fctr Q111580.fctr Q111220.fctr Q110740.fctr
## 6014           No          Yes   Supportive           No           PC
## 6304           NA           NA           NA           NA           NA
##      Q109367.fctr Q109244.fctr Q108950.fctr Q108855.fctr Q108617.fctr
## 6014           NA           NA     Cautious       Umm...           No
## 6304           NA           NA           NA           NA           NA
##      Q108856.fctr Q108754.fctr Q108342.fctr Q108343.fctr Q107869.fctr
## 6014        Space           No       Online          Yes           No
## 6304           NA           NA           NA           NA           NA
##      Q107491.fctr Q106993.fctr Q106997.fctr Q106272.fctr Q106388.fctr
## 6014          Yes          Yes           Yy          Yes           No
## 6304           NA           NA           NA           NA           NA
##      Q106389.fctr Q106042.fctr Q105840.fctr Q105655.fctr Q104996.fctr
## 6014           No          Yes           No          Yes           No
## 6304           NA           NA           NA           NA           NA
##      Q103293.fctr Q102906.fctr Q102674.fctr Q102687.fctr Q102289.fctr
## 6014          Yes           No           No          Yes           No
## 6304           NA           NA           NA           NA           NA
##      Q102089.fctr Q101162.fctr Q101163.fctr Q101596.fctr Q100689.fctr
## 6014          Own    Pessimist          Dad          Yes          Yes
## 6304           NA           NA           NA           NA           NA
##      Q100680.fctr Q100562.fctr Q100010.fctr Q99982.fctr Q99716.fctr
## 6014          Yes           No          Yes        Nope          No
## 6304           NA           NA           NA          NA          NA
##      Q99581.fctr Q99480.fctr Q98869.fctr Q98578.fctr Q98197.fctr
## 6014          No         Yes          NA          NA          NA
## 6304          NA          NA          NA          NA          NA
##      Q98059.fctr Q98078.fctr Q96024.fctr
## 6014          NA          NA         Yes
## 6304          NA          NA          NA
## [1] "min distance(0.9430) pair:"
##      USER_ID Party.fctr Hhold.fctr Q124742.fctr Q124122.fctr Q123621.fctr
## 3335    4153          D        MKy           NA           NA           NA
## 5966    2056       <NA>        MKy           NA           NA           NA
##      Q123464.fctr Q122771.fctr Q122770.fctr Q122769.fctr Q122120.fctr
## 3335           NA           NA           NA           NA           NA
## 5966           NA           NA           NA           NA           NA
##      Q121700.fctr Q121699.fctr Q121011.fctr Q120978.fctr Q120650.fctr
## 3335           NA           NA           NA           NA           NA
## 5966           NA           NA           NA           NA           NA
##      Q120472.fctr Q120379.fctr Q120194.fctr Q120014.fctr Q120012.fctr
## 3335           NA           NA           NA           NA           NA
## 5966           NA           NA           NA           NA           NA
##      Q119851.fctr Q119650.fctr Q119334.fctr Q118892.fctr Q118237.fctr
## 3335           NA           NA           NA           NA           NA
## 5966           NA           NA           NA           NA           NA
##      Q118233.fctr Q118232.fctr Q118117.fctr Q117193.fctr Q117186.fctr
## 3335           NA           NA           NA           NA           NA
## 5966           NA           NA           NA           NA           NA
##      Q116797.fctr Q116881.fctr Q116953.fctr Q116601.fctr Q116441.fctr
## 3335           NA           NA           NA           NA           NA
## 5966           NA           NA           NA           NA           NA
##      Q116448.fctr Q116197.fctr Q115602.fctr Q115777.fctr Q115610.fctr
## 3335           NA           NA           NA           NA           NA
## 5966           NA           NA           NA           NA           NA
##      Q115611.fctr Q115899.fctr Q115390.fctr Q115195.fctr Q114961.fctr
## 3335           NA           NA           NA          Yes          Yes
## 5966           NA           NA           NA           NA           NA
##      Q114748.fctr Q114517.fctr Q114386.fctr Q114152.fctr Q113992.fctr
## 3335           No          Yes   Mysterious          Yes          Yes
## 5966           NA           NA           NA           NA           NA
##      Q113583.fctr Q113584.fctr Q113181.fctr Q112478.fctr Q112512.fctr
## 3335         Talk       People           No           NA           NA
## 5966           NA           NA           NA           NA           NA
##      Q112270.fctr Q111848.fctr Q111580.fctr Q111220.fctr Q110740.fctr
## 3335           NA           NA           NA           NA           NA
## 5966           NA           NA           NA           NA           PC
##      Q109367.fctr Q109244.fctr  Q108950.fctr Q108855.fctr Q108617.fctr
## 3335           No          Yes Risk-friendly           NA           NA
## 5966          Yes          Yes            NA           NA           NA
##      Q108856.fctr Q108754.fctr Q108342.fctr Q108343.fctr Q107869.fctr
## 3335        Space           NA           NA           NA           NA
## 5966           NA           NA           NA           NA           NA
##      Q107491.fctr Q106993.fctr Q106997.fctr Q106272.fctr Q106388.fctr
## 3335           NA           NA           NA           NA           NA
## 5966           NA           NA           NA           NA           NA
##      Q106389.fctr Q106042.fctr Q105840.fctr Q105655.fctr Q104996.fctr
## 3335           NA           NA           NA           NA           NA
## 5966           NA           NA           NA           NA           NA
##      Q103293.fctr Q102906.fctr Q102674.fctr Q102687.fctr Q102289.fctr
## 3335           NA           NA           NA           NA           NA
## 5966           NA           NA           NA           NA           NA
##      Q102089.fctr Q101162.fctr Q101163.fctr Q101596.fctr Q100689.fctr
## 3335           NA           NA           NA           NA           NA
## 5966           NA           NA           NA           NA           NA
##      Q100680.fctr Q100562.fctr Q100010.fctr Q99982.fctr Q99716.fctr
## 3335           NA           NA           NA          NA          NA
## 5966           NA           NA           NA          NA          NA
##      Q99581.fctr Q99480.fctr Q98869.fctr Q98578.fctr Q98197.fctr
## 3335          NA          NA          NA          NA          NA
## 5966          NA          NA          NA          NA          NA
##      Q98059.fctr Q98078.fctr Q96024.fctr
## 3335          NA          NA          NA
## 5966          NA          NA          NA
## [1] "Category: PKn"
## [1] "max distance(0.9770) pair:"
##      USER_ID Party.fctr Hhold.fctr Q124742.fctr Q124122.fctr Q123621.fctr
## 2165    2698          R        PKn           NA           NA           NA
## 5415    6762          D        PKn          Yes          Yes           No
##      Q123464.fctr Q122771.fctr Q122770.fctr Q122769.fctr Q122120.fctr
## 2165           NA           NA           NA           NA           NA
## 5415           No           Pt           No          Yes          Yes
##      Q121700.fctr Q121699.fctr Q121011.fctr Q120978.fctr Q120650.fctr
## 2165           NA           NA           NA           NA           NA
## 5415          Yes          Yes          Yes           No          Yes
##      Q120472.fctr Q120379.fctr Q120194.fctr Q120014.fctr Q120012.fctr
## 2165           NA           NA           NA           NA           NA
## 5415      Science          Yes    Try first          Yes           No
##      Q119851.fctr Q119650.fctr Q119334.fctr Q118892.fctr Q118237.fctr
## 2165           NA           NA           NA          Yes           No
## 5415          Yes       Giving          Yes          Yes           NA
##      Q118233.fctr Q118232.fctr Q118117.fctr Q117193.fctr Q117186.fctr
## 2165           No           Id           No    Odd hours   Hot headed
## 5415           NA           NA           NA           NA           NA
##      Q116797.fctr Q116881.fctr Q116953.fctr Q116601.fctr Q116441.fctr
## 2165          Yes        Happy           No          Yes           No
## 5415           NA           NA           NA           NA           NA
##      Q116448.fctr Q116197.fctr Q115602.fctr Q115777.fctr Q115610.fctr
## 2165           NA         A.M.           NA           NA           NA
## 5415           NA           NA           NA           NA           NA
##      Q115611.fctr Q115899.fctr Q115390.fctr Q115195.fctr Q114961.fctr
## 2165           NA           NA           No          Yes           No
## 5415           NA           NA           NA           NA           NA
##      Q114748.fctr Q114517.fctr Q114386.fctr Q114152.fctr Q113992.fctr
## 2165          Yes          Yes          TMI          Yes          Yes
## 5415           NA           NA           NA           NA           NA
##      Q113583.fctr Q113584.fctr Q113181.fctr Q112478.fctr Q112512.fctr
## 2165         Talk   Technology           NA           No          Yes
## 5415           NA           NA           NA           NA           NA
##      Q112270.fctr Q111848.fctr Q111580.fctr Q111220.fctr Q110740.fctr
## 2165           No          Yes    Demanding          Yes          Mac
## 5415           NA           NA           NA           NA           NA
##      Q109367.fctr Q109244.fctr Q108950.fctr Q108855.fctr Q108617.fctr
## 2165          Yes           No     Cautious       Umm...           No
## 5415           NA           NA           NA           NA           NA
##      Q108856.fctr Q108754.fctr Q108342.fctr Q108343.fctr Q107869.fctr
## 2165    Socialize           No       Online           No          Yes
## 5415           NA           NA           NA           NA           NA
##      Q107491.fctr Q106993.fctr Q106997.fctr Q106272.fctr Q106388.fctr
## 2165          Yes          Yes           Yy          Yes          Yes
## 5415           NA           NA           NA           NA           NA
##      Q106389.fctr Q106042.fctr Q105840.fctr Q105655.fctr Q104996.fctr
## 2165           No          Yes           No           No           No
## 5415           NA           NA           NA           NA           NA
##      Q103293.fctr Q102906.fctr Q102674.fctr Q102687.fctr Q102289.fctr
## 2165           No           No           No          Yes           No
## 5415           NA           NA           NA           NA           NA
##      Q102089.fctr Q101162.fctr Q101163.fctr Q101596.fctr Q100689.fctr
## 2165          Own    Pessimist           NA          Yes          Yes
## 5415           NA           NA           NA           NA           NA
##      Q100680.fctr Q100562.fctr Q100010.fctr Q99982.fctr Q99716.fctr
## 2165          Yes           No          Yes      Check!          No
## 5415           NA           NA           NA          NA          NA
##      Q99581.fctr Q99480.fctr Q98869.fctr Q98578.fctr Q98197.fctr
## 2165          No         Yes          NA         Yes         Yes
## 5415          NA          NA          NA          NA          NA
##      Q98059.fctr Q98078.fctr Q96024.fctr
## 2165         Yes          No         Yes
## 5415          NA          NA          NA
## [1] "min distance(0.9426) pair:"
##      USER_ID Party.fctr Hhold.fctr Q124742.fctr Q124122.fctr Q123621.fctr
## 848     1046          D        PKn           NA           NA           NA
## 3463    4312          D        PKn           NA           NA           NA
##      Q123464.fctr Q122771.fctr Q122770.fctr Q122769.fctr Q122120.fctr
## 848            NA           NA           NA           NA           NA
## 3463           NA           NA           NA           NA           NA
##      Q121700.fctr Q121699.fctr Q121011.fctr Q120978.fctr Q120650.fctr
## 848            NA           NA           NA           NA           NA
## 3463           NA           NA           NA           NA           NA
##      Q120472.fctr Q120379.fctr Q120194.fctr Q120014.fctr Q120012.fctr
## 848            NA           NA           NA           NA           NA
## 3463           NA           NA           NA           NA           NA
##      Q119851.fctr Q119650.fctr Q119334.fctr Q118892.fctr Q118237.fctr
## 848            NA           NA           NA           NA           NA
## 3463           NA           NA           NA           NA           NA
##      Q118233.fctr Q118232.fctr Q118117.fctr Q117193.fctr Q117186.fctr
## 848            NA           NA           NA           NA           NA
## 3463           NA           NA           NA           NA           NA
##      Q116797.fctr Q116881.fctr Q116953.fctr Q116601.fctr Q116441.fctr
## 848            NA           NA           NA           NA           NA
## 3463           NA           NA           NA           NA           NA
##      Q116448.fctr Q116197.fctr Q115602.fctr Q115777.fctr Q115610.fctr
## 848            NA           NA           NA           NA           NA
## 3463           NA           NA           NA           NA           NA
##      Q115611.fctr Q115899.fctr Q115390.fctr Q115195.fctr Q114961.fctr
## 848            NA           NA           NA           NA           NA
## 3463           NA           NA           NA           NA           NA
##      Q114748.fctr Q114517.fctr Q114386.fctr Q114152.fctr Q113992.fctr
## 848            NA           NA           NA           NA           NA
## 3463           NA           NA           NA           NA           NA
##      Q113583.fctr Q113584.fctr Q113181.fctr Q112478.fctr Q112512.fctr
## 848            NA           NA           NA           NA           NA
## 3463           NA           NA           NA           NA           NA
##      Q112270.fctr Q111848.fctr Q111580.fctr Q111220.fctr Q110740.fctr
## 848            NA           NA           NA           NA           NA
## 3463           NA           NA           NA          Yes          Mac
##      Q109367.fctr Q109244.fctr  Q108950.fctr Q108855.fctr Q108617.fctr
## 848           Yes          Yes Risk-friendly         Yes!           No
## 3463          Yes          Yes Risk-friendly         Yes!           No
##      Q108856.fctr Q108754.fctr Q108342.fctr Q108343.fctr Q107869.fctr
## 848         Space           No    In-person          Yes           NA
## 3463        Space          Yes           NA           NA           NA
##      Q107491.fctr Q106993.fctr Q106997.fctr Q106272.fctr Q106388.fctr
## 848            NA           NA           NA           NA           NA
## 3463           NA           NA           NA           NA           NA
##      Q106389.fctr Q106042.fctr Q105840.fctr Q105655.fctr Q104996.fctr
## 848            NA           NA           NA           NA           NA
## 3463           NA           NA           NA           NA           NA
##      Q103293.fctr Q102906.fctr Q102674.fctr Q102687.fctr Q102289.fctr
## 848            NA           NA           NA           NA           NA
## 3463           NA           NA           NA           NA           NA
##      Q102089.fctr Q101162.fctr Q101163.fctr Q101596.fctr Q100689.fctr
## 848            NA           NA           NA           NA           NA
## 3463           NA           NA           NA           NA           NA
##      Q100680.fctr Q100562.fctr Q100010.fctr Q99982.fctr Q99716.fctr
## 848            NA           NA           NA          NA          NA
## 3463           NA           NA           NA          NA          NA
##      Q99581.fctr Q99480.fctr Q98869.fctr Q98578.fctr Q98197.fctr
## 848           NA          NA          NA          NA          NA
## 3463          NA          NA          NA          NA          NA
##      Q98059.fctr Q98078.fctr Q96024.fctr
## 848           NA          NA          No
## 3463          NA          NA          NA
## [1] "Category: PKy"
## [1] "max distance(0.9776) pair:"
##      USER_ID Party.fctr Hhold.fctr Q124742.fctr Q124122.fctr Q123621.fctr
## 1561    1933          R        PKy           NA           NA           NA
## 2346    2921          R        PKy           No           No          Yes
##      Q123464.fctr Q122771.fctr Q122770.fctr Q122769.fctr Q122120.fctr
## 1561           NA           NA           NA           NA           NA
## 2346           No           Pc           No           No          Yes
##      Q121700.fctr Q121699.fctr Q121011.fctr Q120978.fctr Q120650.fctr
## 1561           NA           NA           NA           NA           NA
## 2346           No          Yes          Yes          Yes          Yes
##      Q120472.fctr Q120379.fctr Q120194.fctr Q120014.fctr Q120012.fctr
## 1561           NA           NA           NA           NA           NA
## 2346      Science          Yes  Study first          Yes           No
##      Q119851.fctr Q119650.fctr Q119334.fctr Q118892.fctr Q118237.fctr
## 1561           NA           NA           NA           NA          Yes
## 2346          Yes       Giving          Yes          Yes           No
##      Q118233.fctr Q118232.fctr Q118117.fctr Q117193.fctr Q117186.fctr
## 1561          Yes           Pr           No           NA  Cool headed
## 2346          Yes           Id           No    Odd hours  Cool headed
##      Q116797.fctr Q116881.fctr Q116953.fctr Q116601.fctr Q116441.fctr
## 1561           No        Happy           No          Yes           No
## 2346           NA           NA           NA           NA           NA
##      Q116448.fctr Q116197.fctr Q115602.fctr Q115777.fctr Q115610.fctr
## 1561           No         A.M.          Yes        Start          Yes
## 2346           NA           NA           NA           NA           NA
##      Q115611.fctr Q115899.fctr Q115390.fctr Q115195.fctr Q114961.fctr
## 1561           NA           Cs          Yes           No           No
## 2346           NA           NA           NA           NA           NA
##      Q114748.fctr Q114517.fctr Q114386.fctr Q114152.fctr Q113992.fctr
## 1561          Yes          Yes   Mysterious           No           No
## 2346           NA           NA           NA           NA           NA
##      Q113583.fctr Q113584.fctr Q113181.fctr Q112478.fctr Q112512.fctr
## 1561           NA           NA           NA           NA           NA
## 2346           NA           NA           NA           NA           NA
##      Q112270.fctr Q111848.fctr Q111580.fctr Q111220.fctr Q110740.fctr
## 1561           NA           NA           NA           NA           NA
## 2346           NA           NA           NA           NA           NA
##      Q109367.fctr Q109244.fctr Q108950.fctr Q108855.fctr Q108617.fctr
## 1561           NA           NA           NA           NA           NA
## 2346           NA           NA           NA           NA           NA
##      Q108856.fctr Q108754.fctr Q108342.fctr Q108343.fctr Q107869.fctr
## 1561           NA           NA           NA           NA           NA
## 2346           NA           NA           NA           NA           NA
##      Q107491.fctr Q106993.fctr Q106997.fctr Q106272.fctr Q106388.fctr
## 1561           NA           NA           NA          Yes           No
## 2346           NA           NA           NA           NA           NA
##      Q106389.fctr Q106042.fctr Q105840.fctr Q105655.fctr Q104996.fctr
## 1561           No           No          Yes           No           NA
## 2346           NA           NA           NA           NA           NA
##      Q103293.fctr Q102906.fctr Q102674.fctr Q102687.fctr Q102289.fctr
## 1561           NA           NA           NA           NA           NA
## 2346           NA           NA           NA           NA           NA
##      Q102089.fctr Q101162.fctr Q101163.fctr Q101596.fctr Q100689.fctr
## 1561           NA           NA           NA           NA          Yes
## 2346           NA           NA           NA           NA           NA
##      Q100680.fctr Q100562.fctr Q100010.fctr Q99982.fctr Q99716.fctr
## 1561          Yes           No          Yes      Check!          No
## 2346           NA           NA           NA          NA          NA
##      Q99581.fctr Q99480.fctr Q98869.fctr Q98578.fctr Q98197.fctr
## 1561          No         Yes         Yes          No          No
## 2346          NA          NA          NA          NA          NA
##      Q98059.fctr Q98078.fctr Q96024.fctr
## 1561         Yes         Yes         Yes
## 2346          NA          NA          NA
## [1] "min distance(0.9481) pair:"
##      USER_ID Party.fctr Hhold.fctr Q124742.fctr Q124122.fctr Q123621.fctr
## 2107    2623          D        PKy           NA           NA           NA
## 6815    6244       <NA>        PKy           NA           NA           NA
##      Q123464.fctr Q122771.fctr Q122770.fctr Q122769.fctr Q122120.fctr
## 2107           NA           NA           NA           NA           NA
## 6815           NA           NA           NA           NA           NA
##      Q121700.fctr Q121699.fctr Q121011.fctr Q120978.fctr Q120650.fctr
## 2107           NA           NA          Yes          Yes          Yes
## 6815           NA           NA           NA           NA           NA
##      Q120472.fctr Q120379.fctr Q120194.fctr Q120014.fctr Q120012.fctr
## 2107          Art           NA           NA          Yes          Yes
## 6815           NA           NA           NA           NA           NA
##      Q119851.fctr Q119650.fctr Q119334.fctr Q118892.fctr Q118237.fctr
## 2107          Yes           NA           NA           NA           NA
## 6815           NA           NA           NA           NA           NA
##      Q118233.fctr Q118232.fctr Q118117.fctr Q117193.fctr Q117186.fctr
## 2107           NA           NA           NA           NA           NA
## 6815           NA           NA           NA           NA           NA
##      Q116797.fctr Q116881.fctr Q116953.fctr Q116601.fctr Q116441.fctr
## 2107           NA           NA           NA           NA           NA
## 6815           NA           NA           NA           NA           NA
##      Q116448.fctr Q116197.fctr Q115602.fctr Q115777.fctr Q115610.fctr
## 2107           NA           NA           NA           NA          Yes
## 6815           NA           NA           NA           NA           NA
##      Q115611.fctr Q115899.fctr Q115390.fctr Q115195.fctr Q114961.fctr
## 2107           No           NA           NA          Yes          Yes
## 6815           NA           NA           NA           NA           NA
##      Q114748.fctr Q114517.fctr Q114386.fctr Q114152.fctr Q113992.fctr
## 2107           No          Yes          TMI          Yes          Yes
## 6815           NA           NA           NA           NA           NA
##      Q113583.fctr Q113584.fctr Q113181.fctr Q112478.fctr Q112512.fctr
## 2107         Talk           NA           NA           NA           NA
## 6815           NA           NA           NA           NA           NA
##      Q112270.fctr Q111848.fctr Q111580.fctr Q111220.fctr Q110740.fctr
## 2107           NA           NA           NA           NA           PC
## 6815           NA           NA           NA           NA           PC
##      Q109367.fctr Q109244.fctr  Q108950.fctr Q108855.fctr Q108617.fctr
## 2107          Yes          Yes            NA       Umm...           NA
## 6815          Yes          Yes Risk-friendly       Umm...           No
##      Q108856.fctr Q108754.fctr Q108342.fctr Q108343.fctr Q107869.fctr
## 2107           NA          Yes           NA           No           NA
## 6815        Space           No           NA           NA           NA
##      Q107491.fctr Q106993.fctr Q106997.fctr Q106272.fctr Q106388.fctr
## 2107           NA           NA           NA           NA           NA
## 6815           NA           NA           NA           NA           NA
##      Q106389.fctr Q106042.fctr Q105840.fctr Q105655.fctr Q104996.fctr
## 2107           NA           NA           NA           NA           NA
## 6815           NA           NA           NA           NA           NA
##      Q103293.fctr Q102906.fctr Q102674.fctr Q102687.fctr Q102289.fctr
## 2107           NA           NA           NA           NA           NA
## 6815           NA           NA           NA           NA           NA
##      Q102089.fctr Q101162.fctr Q101163.fctr Q101596.fctr Q100689.fctr
## 2107           NA           NA           NA           NA           NA
## 6815           NA           NA           NA           NA           NA
##      Q100680.fctr Q100562.fctr Q100010.fctr Q99982.fctr Q99716.fctr
## 2107           NA           NA           NA          NA          NA
## 6815           NA           NA           NA          NA          NA
##      Q99581.fctr Q99480.fctr Q98869.fctr Q98578.fctr Q98197.fctr
## 2107          NA          NA          NA          NA          NA
## 6815          NA          NA          NA          NA          NA
##      Q98059.fctr Q98078.fctr Q96024.fctr
## 2107          NA          NA          NA
## 6815          NA          NA          NA
## [1] "Category: SKn"
## [1] "max distance(0.9786) pair:"
##      USER_ID Party.fctr Hhold.fctr Q124742.fctr Q124122.fctr Q123621.fctr
## 3905    4863          R        SKn           NA           NA           NA
## 4010    4997          D        SKn           NA           NA           No
##      Q123464.fctr Q122771.fctr Q122770.fctr Q122769.fctr Q122120.fctr
## 3905           NA           NA           NA           NA           NA
## 4010           No           Pt          Yes           No           NA
##      Q121700.fctr Q121699.fctr Q121011.fctr Q120978.fctr Q120650.fctr
## 3905           NA           NA           NA           NA           NA
## 4010          Yes          Yes          Yes          Yes          Yes
##      Q120472.fctr Q120379.fctr Q120194.fctr Q120014.fctr Q120012.fctr
## 3905           NA           NA           NA           NA           NA
## 4010           NA          Yes           NA           NA           NA
##      Q119851.fctr Q119650.fctr Q119334.fctr Q118892.fctr Q118237.fctr
## 3905           NA           NA           NA           NA           No
## 4010           NA           NA          Yes           NA           NA
##      Q118233.fctr Q118232.fctr Q118117.fctr   Q117193.fctr Q117186.fctr
## 3905          Yes           Id           No Standard hours  Cool headed
## 4010           NA           Id           No             NA  Cool headed
##      Q116797.fctr Q116881.fctr Q116953.fctr Q116601.fctr Q116441.fctr
## 3905          Yes        Happy          Yes          Yes          Yes
## 4010           NA           NA           NA          Yes           NA
##      Q116448.fctr Q116197.fctr Q115602.fctr Q115777.fctr Q115610.fctr
## 3905          Yes         P.M.           NA           NA           NA
## 4010           No         P.M.           NA        Start           NA
##      Q115611.fctr Q115899.fctr Q115390.fctr Q115195.fctr Q114961.fctr
## 3905           NA           NA           NA           NA           NA
## 4010           NA           NA           NA          Yes           No
##      Q114748.fctr Q114517.fctr Q114386.fctr Q114152.fctr Q113992.fctr
## 3905           NA           NA           NA           NA           NA
## 4010           No           No   Mysterious           NA           NA
##      Q113583.fctr Q113584.fctr Q113181.fctr Q112478.fctr Q112512.fctr
## 3905           NA           NA           NA           NA           NA
## 4010        Tunes   Technology           NA           NA          Yes
##      Q112270.fctr Q111848.fctr Q111580.fctr Q111220.fctr Q110740.fctr
## 3905           NA           NA           NA           NA           NA
## 4010          Yes           NA           NA           NA           NA
##      Q109367.fctr Q109244.fctr Q108950.fctr Q108855.fctr Q108617.fctr
## 3905           NA           NA           NA           NA           NA
## 4010           NA           NA           NA         Yes!           No
##      Q108856.fctr Q108754.fctr Q108342.fctr Q108343.fctr Q107869.fctr
## 3905           NA           NA           NA           NA           No
## 4010           NA           No    In-person           NA           NA
##      Q107491.fctr Q106993.fctr Q106997.fctr Q106272.fctr Q106388.fctr
## 3905           No          Yes           Gr          Yes          Yes
## 4010           NA           NA           NA           NA           NA
##      Q106389.fctr Q106042.fctr Q105840.fctr Q105655.fctr Q104996.fctr
## 3905          Yes          Yes          Yes          Yes           NA
## 4010           NA           NA           NA           NA           NA
##      Q103293.fctr Q102906.fctr Q102674.fctr Q102687.fctr Q102289.fctr
## 3905           No          Yes          Yes          Yes           No
## 4010           NA           NA           NA           NA           NA
##      Q102089.fctr Q101162.fctr Q101163.fctr Q101596.fctr Q100689.fctr
## 3905          Own    Pessimist          Mom          Yes          Yes
## 4010           NA     Optimist          Dad           NA           No
##      Q100680.fctr Q100562.fctr Q100010.fctr Q99982.fctr Q99716.fctr
## 3905           No           No          Yes        Nope         Yes
## 4010           No          Yes           NA      Check!          No
##      Q99581.fctr Q99480.fctr Q98869.fctr Q98578.fctr Q98197.fctr
## 3905         Yes         Yes          No          NA          NA
## 4010          No          NA          NA          NA          NA
##      Q98059.fctr Q98078.fctr Q96024.fctr
## 3905          NA          NA          NA
## 4010         Yes         Yes         Yes
## [1] "min distance(0.9380) pair:"
##      USER_ID Party.fctr Hhold.fctr Q124742.fctr Q124122.fctr Q123621.fctr
## 2524    3142          D        SKn           NA           NA           NA
## 2712    3375          D        SKn           NA           NA           NA
##      Q123464.fctr Q122771.fctr Q122770.fctr Q122769.fctr Q122120.fctr
## 2524           NA           NA           NA           NA           NA
## 2712           NA           NA           NA           NA           NA
##      Q121700.fctr Q121699.fctr Q121011.fctr Q120978.fctr Q120650.fctr
## 2524           NA           NA           NA           NA           NA
## 2712           NA           NA           NA           NA           NA
##      Q120472.fctr Q120379.fctr Q120194.fctr Q120014.fctr Q120012.fctr
## 2524           NA           NA           NA           NA           NA
## 2712           NA           NA           NA           NA           NA
##      Q119851.fctr Q119650.fctr Q119334.fctr Q118892.fctr Q118237.fctr
## 2524           NA           NA           NA           NA           NA
## 2712           NA           NA           NA           NA           NA
##      Q118233.fctr Q118232.fctr Q118117.fctr Q117193.fctr Q117186.fctr
## 2524           NA           NA           NA           NA           NA
## 2712           NA           NA           NA           NA           NA
##      Q116797.fctr Q116881.fctr Q116953.fctr Q116601.fctr Q116441.fctr
## 2524           NA           NA           NA           NA           NA
## 2712           NA           NA           NA           NA           NA
##      Q116448.fctr Q116197.fctr Q115602.fctr Q115777.fctr Q115610.fctr
## 2524           NA           NA           NA           NA           NA
## 2712           NA           NA           NA           NA           NA
##      Q115611.fctr Q115899.fctr Q115390.fctr Q115195.fctr Q114961.fctr
## 2524           NA           NA           NA           NA           NA
## 2712           NA           NA           NA           NA           NA
##      Q114748.fctr Q114517.fctr Q114386.fctr Q114152.fctr Q113992.fctr
## 2524           NA           NA           NA           NA           NA
## 2712           NA           NA           NA           NA           NA
##      Q113583.fctr Q113584.fctr Q113181.fctr Q112478.fctr Q112512.fctr
## 2524           NA           NA           NA           NA           NA
## 2712           NA           NA           NA           NA           NA
##      Q112270.fctr Q111848.fctr Q111580.fctr Q111220.fctr Q110740.fctr
## 2524           NA           NA           NA           NA           NA
## 2712           NA           NA           NA           NA           NA
##      Q109367.fctr Q109244.fctr Q108950.fctr Q108855.fctr Q108617.fctr
## 2524          Yes          Yes           NA       Umm...           NA
## 2712           No          Yes     Cautious           NA           NA
##      Q108856.fctr Q108754.fctr Q108342.fctr Q108343.fctr Q107869.fctr
## 2524           NA           No       Online          Yes           NA
## 2712           NA           No           NA           NA           NA
##      Q107491.fctr Q106993.fctr Q106997.fctr Q106272.fctr Q106388.fctr
## 2524           NA           NA           NA           NA           NA
## 2712           NA           NA           NA           NA           NA
##      Q106389.fctr Q106042.fctr Q105840.fctr Q105655.fctr Q104996.fctr
## 2524           NA           NA           NA           NA           NA
## 2712           NA           NA           NA           NA           NA
##      Q103293.fctr Q102906.fctr Q102674.fctr Q102687.fctr Q102289.fctr
## 2524           NA           NA           NA           NA           NA
## 2712           NA           NA           NA           NA           NA
##      Q102089.fctr Q101162.fctr Q101163.fctr Q101596.fctr Q100689.fctr
## 2524           NA           NA           NA           NA           NA
## 2712           NA           NA           NA           NA           NA
##      Q100680.fctr Q100562.fctr Q100010.fctr Q99982.fctr Q99716.fctr
## 2524           NA           NA           NA          NA          NA
## 2712           NA           NA           NA          NA          NA
##      Q99581.fctr Q99480.fctr Q98869.fctr Q98578.fctr Q98197.fctr
## 2524          NA          NA          NA          NA          NA
## 2712          NA          NA          NA          NA          NA
##      Q98059.fctr Q98078.fctr Q96024.fctr
## 2524          NA          NA          NA
## 2712          NA          NA          NA
## [1] "Category: SKy"
## [1] "max distance(0.9776) pair:"
##      USER_ID Party.fctr Hhold.fctr Q124742.fctr Q124122.fctr Q123621.fctr
## 2213    2759          R        SKy           NA           NA           NA
## 6921    6795       <NA>        SKy          Yes          Yes           No
##      Q123464.fctr Q122771.fctr Q122770.fctr Q122769.fctr Q122120.fctr
## 2213           NA           NA           NA           NA           NA
## 6921           No           Pc          Yes          Yes          Yes
##      Q121700.fctr Q121699.fctr Q121011.fctr Q120978.fctr Q120650.fctr
## 2213           NA           NA           NA           NA           NA
## 6921          Yes           No          Yes          Yes          Yes
##      Q120472.fctr Q120379.fctr Q120194.fctr Q120014.fctr Q120012.fctr
## 2213           NA           NA           NA           NA           NA
## 6921      Science          Yes    Try first          Yes          Yes
##      Q119851.fctr Q119650.fctr Q119334.fctr Q118892.fctr Q118237.fctr
## 2213           NA           NA           NA           NA           NA
## 6921           No       Giving          Yes          Yes           NA
##      Q118233.fctr Q118232.fctr Q118117.fctr Q117193.fctr Q117186.fctr
## 2213           NA           NA           NA           NA           NA
## 6921           NA           NA           NA           NA           NA
##      Q116797.fctr Q116881.fctr Q116953.fctr Q116601.fctr Q116441.fctr
## 2213           NA           NA           NA           NA           NA
## 6921           NA           NA           NA           NA           NA
##      Q116448.fctr Q116197.fctr Q115602.fctr Q115777.fctr Q115610.fctr
## 2213           NA           NA           NA           NA           NA
## 6921           NA           NA           NA           NA           NA
##      Q115611.fctr Q115899.fctr Q115390.fctr Q115195.fctr Q114961.fctr
## 2213           NA           NA           NA           NA           NA
## 6921           NA           NA           NA           NA           NA
##      Q114748.fctr Q114517.fctr Q114386.fctr Q114152.fctr Q113992.fctr
## 2213           NA           NA           NA           NA           NA
## 6921           NA           NA           NA           NA           NA
##      Q113583.fctr Q113584.fctr Q113181.fctr Q112478.fctr Q112512.fctr
## 2213           NA           NA           NA           NA           NA
## 6921           NA           NA           NA           NA           NA
##      Q112270.fctr Q111848.fctr Q111580.fctr Q111220.fctr Q110740.fctr
## 2213           NA           NA    Demanding          Yes           PC
## 6921           NA           NA           NA           NA           NA
##      Q109367.fctr Q109244.fctr Q108950.fctr Q108855.fctr Q108617.fctr
## 2213           NA           NA           NA           NA           NA
## 6921           NA           NA           NA           NA           NA
##      Q108856.fctr Q108754.fctr Q108342.fctr Q108343.fctr Q107869.fctr
## 2213           NA           NA           NA           NA          Yes
## 6921           NA           NA           NA           NA           NA
##      Q107491.fctr Q106993.fctr Q106997.fctr Q106272.fctr Q106388.fctr
## 2213          Yes          Yes           Gr          Yes           No
## 6921           NA           NA           NA           NA           NA
##      Q106389.fctr Q106042.fctr Q105840.fctr Q105655.fctr Q104996.fctr
## 2213          Yes          Yes           No           No           No
## 6921           NA           NA           NA           NA           NA
##      Q103293.fctr Q102906.fctr Q102674.fctr Q102687.fctr Q102289.fctr
## 2213          Yes          Yes          Yes           No           No
## 6921           NA           NA           NA           NA           NA
##      Q102089.fctr Q101162.fctr Q101163.fctr Q101596.fctr Q100689.fctr
## 2213          Own    Pessimist          Mom          Yes          Yes
## 6921           NA           NA           NA           NA           NA
##      Q100680.fctr Q100562.fctr Q100010.fctr Q99982.fctr Q99716.fctr
## 2213          Yes          Yes          Yes        Nope          No
## 6921           NA           NA           NA          NA          NA
##      Q99581.fctr Q99480.fctr Q98869.fctr Q98578.fctr Q98197.fctr
## 2213         Yes         Yes         Yes          No          No
## 6921          NA          NA          NA          NA          NA
##      Q98059.fctr Q98078.fctr Q96024.fctr
## 2213         Yes          No          No
## 6921          NA          NA          NA
## [1] "min distance(0.9466) pair:"
##      USER_ID Party.fctr Hhold.fctr Q124742.fctr Q124122.fctr Q123621.fctr
## 2539    3157          R        SKy           NA           NA           NA
## 3850    4797          D        SKy           NA           NA           NA
##      Q123464.fctr Q122771.fctr Q122770.fctr Q122769.fctr Q122120.fctr
## 2539           NA           NA           NA           NA           NA
## 3850           NA           NA           NA           NA           NA
##      Q121700.fctr Q121699.fctr Q121011.fctr Q120978.fctr Q120650.fctr
## 2539           NA           NA           NA           NA           NA
## 3850           NA           NA           NA           NA           NA
##      Q120472.fctr Q120379.fctr Q120194.fctr Q120014.fctr Q120012.fctr
## 2539           NA           NA           NA           NA           NA
## 3850           NA           NA           NA           NA           NA
##      Q119851.fctr Q119650.fctr Q119334.fctr Q118892.fctr Q118237.fctr
## 2539           NA           NA           NA           NA           NA
## 3850           NA           NA           NA           NA           NA
##      Q118233.fctr Q118232.fctr Q118117.fctr Q117193.fctr Q117186.fctr
## 2539           NA           NA           NA           NA           NA
## 3850           NA           NA           NA           NA           NA
##      Q116797.fctr Q116881.fctr Q116953.fctr Q116601.fctr Q116441.fctr
## 2539           NA           NA           NA           NA           NA
## 3850           NA           NA           NA           NA           NA
##      Q116448.fctr Q116197.fctr Q115602.fctr Q115777.fctr Q115610.fctr
## 2539           NA           NA           NA           NA           NA
## 3850           NA           NA           NA           NA           NA
##      Q115611.fctr Q115899.fctr Q115390.fctr Q115195.fctr Q114961.fctr
## 2539           NA           NA           NA           NA           NA
## 3850           NA           NA           NA           NA           NA
##      Q114748.fctr Q114517.fctr Q114386.fctr Q114152.fctr Q113992.fctr
## 2539           NA           NA           NA           NA           NA
## 3850          Yes           No   Mysterious           No           No
##      Q113583.fctr Q113584.fctr Q113181.fctr Q112478.fctr Q112512.fctr
## 2539           NA           NA           NA           NA           NA
## 3850         Talk       People           NA           NA           NA
##      Q112270.fctr Q111848.fctr Q111580.fctr Q111220.fctr Q110740.fctr
## 2539           NA           No           NA           NA           NA
## 3850           NA           No           NA           No          Mac
##      Q109367.fctr Q109244.fctr  Q108950.fctr Q108855.fctr Q108617.fctr
## 2539           NA          Yes            NA           NA           NA
## 3850           NA          Yes Risk-friendly       Umm...          Yes
##      Q108856.fctr Q108754.fctr Q108342.fctr Q108343.fctr Q107869.fctr
## 2539           NA           NA           NA           NA           NA
## 3850        Space          Yes    In-person           No          Yes
##      Q107491.fctr Q106993.fctr Q106997.fctr Q106272.fctr Q106388.fctr
## 2539           NA           NA           NA           NA           NA
## 3850           No          Yes           Yy           No           No
##      Q106389.fctr Q106042.fctr Q105840.fctr Q105655.fctr Q104996.fctr
## 2539           NA           NA           NA           NA           NA
## 3850          Yes          Yes          Yes           No           No
##      Q103293.fctr Q102906.fctr Q102674.fctr Q102687.fctr Q102289.fctr
## 2539           NA           NA           NA           NA           NA
## 3850           NA           NA           NA           NA           NA
##      Q102089.fctr Q101162.fctr Q101163.fctr Q101596.fctr Q100689.fctr
## 2539         Rent           NA           NA           NA           NA
## 3850           NA           NA           NA           NA           NA
##      Q100680.fctr Q100562.fctr Q100010.fctr Q99982.fctr Q99716.fctr
## 2539           NA           NA           NA          NA          NA
## 3850           NA           NA           NA          NA          NA
##      Q99581.fctr Q99480.fctr Q98869.fctr Q98578.fctr Q98197.fctr
## 2539          NA          NA          NA          NA          NA
## 3850          NA          NA          NA          NA          NA
##      Q98059.fctr Q98078.fctr Q96024.fctr
## 2539          NA          NA          NA
## 3850          NA          NA          NA
##    Hhold.fctr .clusterid Hhold.fctr.clusterid   R   D  .entropy .knt
## 1           N          1                  N_1 129 128 0.6931396  257
## 2           N          2                  N_2  54  45 0.6890092   99
## 3           N          3                  N_3  37  57 0.6703386   94
## 4         MKn          1                MKn_1 159 166 0.6929152  325
## 5         MKn          2                MKn_2  31 110 0.5267284  141
## 6         MKn          3                MKn_3  76  43 0.6541879  119
## 7         MKn          4                MKn_4  42  25 0.6606028   67
## 8         MKy          1                MKy_1 560 508 0.6919614 1068
## 9         MKy          2                MKy_2 230 126 0.6498471  356
## 10        MKy          3                MKy_3  52 118 0.6157663  170
## 11        PKn          1                PKn_1  16  30 0.6460905   46
## 12        PKn          2                PKn_2   4  43 0.2910671   47
## 13        PKn          3                PKn_3  16  26 0.6645284   42
## 14        PKn          4                PKn_4  13  32 0.6011538   45
## 15        PKy          1                PKy_1   9  14 0.6693280   23
## 16        PKy          2                PKy_2  13   8 0.6645284   21
## 17        PKy          3                PKy_3   4  13 0.5455946   17
## 18        SKn          1                SKn_1 513 492 0.6929289 1005
## 19        SKn          2                SKn_2 413 452 0.6921304  865
## 20        SKn          3                SKn_3 165 396 0.6057975  561
## 21        SKy          1                SKy_1  43  50 0.6903118   93
## 22        SKy          2                SKy_2  25  25 0.6931472   50
## 23        SKy          3                SKy_3  13  44 0.5369340   57
## [1] "glbObsAll$Hhold.fctr$.clusterid Entropy: 0.6658 (97.0682 pct)"

##                     label step_major step_minor label_minor     bgn
## 1            cluster.data          1          0           0   9.593
## 2 partition.data.training          2          0           0 133.130
##       end elapsed
## 1 133.129 123.537
## 2      NA      NA

Step 2.0: partition data training

## [1] "partition.data.training chunk: setup: elapsed: 0.00 secs"

## Loading required package: sqldf

## Loading required package: gsubfn

## Loading required package: proto

## Loading required package: RSQLite

## Loading required package: DBI

## Loading required package: tcltk

## Loading required package: reshape2

## [1] "partition.data.training chunk: strata_mtrx complete: elapsed: 6.15 secs"
## [1] "partition.data.training chunk: obs_freq_df complete: elapsed: 6.15 secs"

## Loading required package: sampling

## 
## Attaching package: 'sampling'

## The following object is masked from 'package:caret':
## 
##     cluster

## [1] "lclgetMatrixCorrelation: duration: 44.619000 secs"
## [1] "cor of Fit vs. OOB: 1.0000"
## [1] "lclgetMatrixCorrelation: duration: 15.369000 secs"
## [1] "cor of New vs. OOB: 1.0000"
## [1] "lclgetMatrixCorrelation: duration: 52.911000 secs"
## [1] "cor of Fit vs. New: 1.0000"

## [1] "partition.data.training chunk: Fit/OOB partition complete: elapsed: 119.73 secs"

##     Party.Democrat Party.Republican Party.NA
##                 NA               NA     1392
## Fit           2357             2091       NA
## OOB            594              526       NA
##     Party.Democrat Party.Republican Party.NA
##                 NA               NA        1
## Fit      0.5299011        0.4700989       NA
## OOB      0.5303571        0.4696429       NA
##   Hhold.fctr .n.Fit .n.OOB .n.Tst .freqRatio.Fit .freqRatio.OOB
## 6        SKn   1920    511    638     0.43165468    0.456250000
## 2        MKy   1296    298    371     0.29136691    0.266071429
## 1        MKn    516    136    169     0.11600719    0.121428571
## 3          N    367     83    102     0.08250899    0.074107143
## 7        SKy    147     53     65     0.03304856    0.047321429
## 4        PKn    150     30     37     0.03372302    0.026785714
## 5        PKy     52      9     10     0.01169065    0.008035714
##   .freqRatio.Tst
## 6    0.458333333
## 2    0.266522989
## 1    0.121408046
## 3    0.073275862
## 7    0.046695402
## 4    0.026580460
## 5    0.007183908

## [1] "glbObsAll: "

## [1] 6960  221

## [1] "glbObsTrn: "

## [1] 5568  221

## [1] "glbObsFit: "

## [1] 4448  220

## [1] "glbObsOOB: "

## [1] 1120  220

## [1] "glbObsNew: "

## [1] 1392  220

## [1] "partition.data.training chunk: teardown: elapsed: 120.73 secs"

##                     label step_major step_minor label_minor     bgn
## 2 partition.data.training          2          0           0 133.130
## 3         select.features          3          0           0 253.954
##       end elapsed
## 2 253.954 120.824
## 3      NA      NA

Step 3.0: select features

## [1] "cor(Q98059.fctr, Q98078.fctr)=0.7689"
## [1] "cor(Party.fctr, Q98059.fctr)=0.0172"
## [1] "cor(Party.fctr, Q98078.fctr)=0.0257"

## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glbObsTrn, : Identified Q98059.fctr as highly correlated with Q98078.fctr

## [1] "cor(Q99480.fctr, Q99581.fctr)=0.7660"
## [1] "cor(Party.fctr, Q99480.fctr)=-0.0344"
## [1] "cor(Party.fctr, Q99581.fctr)=-0.0104"

## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glbObsTrn, : Identified Q99581.fctr as highly correlated with Q99480.fctr

## [1] "cor(Q108855.fctr, Q108856.fctr)=0.7430"
## [1] "cor(Party.fctr, Q108855.fctr)=-0.0371"
## [1] "cor(Party.fctr, Q108856.fctr)=-0.0140"

## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glbObsTrn, : Identified Q108856.fctr as highly correlated with Q108855.fctr

## [1] "cor(Q122770.fctr, Q122771.fctr)=0.7379"
## [1] "cor(Party.fctr, Q122770.fctr)=-0.0195"
## [1] "cor(Party.fctr, Q122771.fctr)=-0.0348"

## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glbObsTrn, : Identified Q122770.fctr as highly correlated with Q122771.fctr

## [1] "cor(Q106272.fctr, Q106388.fctr)=0.7339"
## [1] "cor(Party.fctr, Q106272.fctr)=-0.0401"
## [1] "cor(Party.fctr, Q106388.fctr)=-0.0342"

## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glbObsTrn, : Identified Q106388.fctr as highly correlated with Q106272.fctr

## [1] "cor(Q100680.fctr, Q100689.fctr)=0.7292"
## [1] "cor(Party.fctr, Q100680.fctr)=0.0158"
## [1] "cor(Party.fctr, Q100689.fctr)=0.0257"

## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glbObsTrn, : Identified Q100680.fctr as highly correlated with Q100689.fctr

## [1] "cor(Q99480.fctr, Q99716.fctr)=0.7252"
## [1] "cor(Party.fctr, Q99480.fctr)=-0.0344"
## [1] "cor(Party.fctr, Q99716.fctr)=0.0209"

## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glbObsTrn, : Identified Q99716.fctr as highly correlated with Q99480.fctr

## [1] "cor(Q120472.fctr, Q120650.fctr)=0.7126"
## [1] "cor(Party.fctr, Q120472.fctr)=-0.0462"
## [1] "cor(Party.fctr, Q120650.fctr)=-0.0271"

## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glbObsTrn, : Identified Q120650.fctr as highly correlated with Q120472.fctr

## [1] "cor(Q98869.fctr, Q99480.fctr)=0.7084"
## [1] "cor(Party.fctr, Q98869.fctr)=-0.0277"
## [1] "cor(Party.fctr, Q99480.fctr)=-0.0344"

## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glbObsTrn, : Identified Q98869.fctr as highly correlated with Q99480.fctr

## [1] "cor(Q123464.fctr, Q123621.fctr)=0.7078"
## [1] "cor(Party.fctr, Q123464.fctr)=-0.0136"
## [1] "cor(Party.fctr, Q123621.fctr)=-0.0255"

## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glbObsTrn, : Identified Q123464.fctr as highly correlated with Q123621.fctr

## [1] "cor(Q108754.fctr, Q108855.fctr)=0.7005"
## [1] "cor(Party.fctr, Q108754.fctr)=-0.0081"
## [1] "cor(Party.fctr, Q108855.fctr)=-0.0371"

## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glbObsTrn, : Identified Q108754.fctr as highly correlated with Q108855.fctr

##                         cor.y exclude.as.feat    cor.y.abs   cor.high.X
## Q109244.fctr     0.1203812469               0 0.1203812469         <NA>
## .clusterid       0.1012091920               1 0.1012091920         <NA>
## .clusterid.fctr  0.1012091920               0 0.1012091920         <NA>
## Hhold.fctr       0.0511386673               0 0.0511386673         <NA>
## Edn.fctr         0.0359295351               0 0.0359295351         <NA>
## Q101163.fctr     0.0295046473               0 0.0295046473         <NA>
## Q100689.fctr     0.0256915080               0 0.0256915080         <NA>
## Q98078.fctr      0.0256516490               0 0.0256516490         <NA>
## Q99716.fctr      0.0209286674               0 0.0209286674  Q99480.fctr
## Q120379.fctr     0.0206291292               0 0.0206291292         <NA>
## Q121699.fctr     0.0196933075               0 0.0196933075         <NA>
## Q105840.fctr     0.0195569165               0 0.0195569165         <NA>
## Q113583.fctr     0.0191894717               0 0.0191894717         <NA>
## Q115195.fctr     0.0174522586               0 0.0174522586         <NA>
## Q102089.fctr     0.0174087944               0 0.0174087944         <NA>
## Q98059.fctr      0.0171637755               0 0.0171637755  Q98078.fctr
## Q114386.fctr     0.0168013326               0 0.0168013326         <NA>
## Q100680.fctr     0.0157762454               0 0.0157762454 Q100689.fctr
## Q108342.fctr     0.0151842510               0 0.0151842510         <NA>
## Q111848.fctr     0.0141099384               0 0.0141099384         <NA>
## YOB.Age.fctr     0.0129198495               0 0.0129198495         <NA>
## Q118892.fctr     0.0125250379               0 0.0125250379         <NA>
## Q102687.fctr     0.0120079165               0 0.0120079165         <NA>
## Q115390.fctr     0.0119300319               0 0.0119300319         <NA>
## Q119851.fctr     0.0093381833               0 0.0093381833         <NA>
## Q114517.fctr     0.0084741753               0 0.0084741753         <NA>
## Q120012.fctr     0.0084652930               0 0.0084652930         <NA>
## Q109367.fctr     0.0080456026               0 0.0080456026         <NA>
## Q114961.fctr     0.0079206587               0 0.0079206587         <NA>
## Q121700.fctr     0.0067756198               0 0.0067756198         <NA>
## Q124122.fctr     0.0061257448               0 0.0061257448         <NA>
## Q111220.fctr     0.0055758571               0 0.0055758571         <NA>
## Q113992.fctr     0.0041479796               0 0.0041479796         <NA>
## Q121011.fctr     0.0037329030               0 0.0037329030         <NA>
## Q106042.fctr     0.0032327194               0 0.0032327194         <NA>
## Q116448.fctr     0.0031731051               0 0.0031731051         <NA>
## Q116601.fctr     0.0022379241               0 0.0022379241         <NA>
## Q104996.fctr     0.0012202806               0 0.0012202806         <NA>
## Q102906.fctr     0.0011540297               0 0.0011540297         <NA>
## Q113584.fctr     0.0011387024               0 0.0011387024         <NA>
## Q108950.fctr     0.0010567028               0 0.0010567028         <NA>
## Q102674.fctr     0.0009759844               0 0.0009759844         <NA>
## Q103293.fctr     0.0005915534               0 0.0005915534         <NA>
## Q112478.fctr     0.0001517248               0 0.0001517248         <NA>
## Q114748.fctr    -0.0008477228               0 0.0008477228         <NA>
## Q107491.fctr    -0.0014031814               0 0.0014031814         <NA>
## Q100562.fctr    -0.0017132769               0 0.0017132769         <NA>
## Q108617.fctr    -0.0024119725               0 0.0024119725         <NA>
## Q100010.fctr    -0.0024291540               0 0.0024291540         <NA>
## Q115602.fctr    -0.0027844465               0 0.0027844465         <NA>
## Q116953.fctr    -0.0029786716               0 0.0029786716         <NA>
## Q115610.fctr    -0.0035255582               0 0.0035255582         <NA>
## Q106997.fctr    -0.0041749086               0 0.0041749086         <NA>
## Q120978.fctr    -0.0044187616               0 0.0044187616         <NA>
## Q112512.fctr    -0.0056768212               0 0.0056768212         <NA>
## Q108343.fctr    -0.0060665340               0 0.0060665340         <NA>
## Q96024.fctr     -0.0069116541               0 0.0069116541         <NA>
## Q106389.fctr    -0.0077498918               0 0.0077498918         <NA>
## .rnorm          -0.0078039520               0 0.0078039520         <NA>
## Q108754.fctr    -0.0080847764               0 0.0080847764 Q108855.fctr
## Q98578.fctr     -0.0081164509               0 0.0081164509         <NA>
## Q101162.fctr    -0.0099412952               0 0.0099412952         <NA>
## Q115777.fctr    -0.0101315203               0 0.0101315203         <NA>
## Q99581.fctr     -0.0103662478               0 0.0103662478  Q99480.fctr
## Q124742.fctr    -0.0111642906               0 0.0111642906         <NA>
## Q116797.fctr    -0.0112749656               0 0.0112749656         <NA>
## Q112270.fctr    -0.0116157798               0 0.0116157798         <NA>
## YOB             -0.0116828198               1 0.0116828198         <NA>
## Q118237.fctr    -0.0117079669               0 0.0117079669         <NA>
## Q119650.fctr    -0.0125645475               0 0.0125645475         <NA>
## Q111580.fctr    -0.0132382335               0 0.0132382335         <NA>
## Q123464.fctr    -0.0136140083               0 0.0136140083 Q123621.fctr
## Q117193.fctr    -0.0138241599               0 0.0138241599         <NA>
## Q99982.fctr     -0.0139727928               0 0.0139727928         <NA>
## Q108856.fctr    -0.0140363785               0 0.0140363785 Q108855.fctr
## Q118233.fctr    -0.0147269325               0 0.0147269325         <NA>
## Q102289.fctr    -0.0155850393               0 0.0155850393         <NA>
## Q116197.fctr    -0.0158561766               0 0.0158561766         <NA>
## Income.fctr     -0.0159635458               0 0.0159635458         <NA>
## Q118232.fctr    -0.0171321152               0 0.0171321152         <NA>
## Q120194.fctr    -0.0172986920               0 0.0172986920         <NA>
## Q114152.fctr    -0.0175013163               0 0.0175013163         <NA>
## Q122770.fctr    -0.0194639697               0 0.0194639697 Q122771.fctr
## Q117186.fctr    -0.0198853672               0 0.0198853672         <NA>
## Q105655.fctr    -0.0198994078               0 0.0198994078         <NA>
## Q106993.fctr    -0.0207428635               0 0.0207428635         <NA>
## Q119334.fctr    -0.0226894034               0 0.0226894034         <NA>
## Q122120.fctr    -0.0229287700               0 0.0229287700         <NA>
## Q116441.fctr    -0.0237358205               0 0.0237358205         <NA>
## Q118117.fctr    -0.0253544150               0 0.0253544150         <NA>
## Q123621.fctr    -0.0255329743               0 0.0255329743         <NA>
## Q122769.fctr    -0.0259739146               0 0.0259739146         <NA>
## Q120650.fctr    -0.0270889067               0 0.0270889067 Q120472.fctr
## Q98869.fctr     -0.0276734114               0 0.0276734114  Q99480.fctr
## .pos            -0.0302037138               1 0.0302037138         <NA>
## USER_ID         -0.0302304868               1 0.0302304868         <NA>
## Q107869.fctr    -0.0304661021               0 0.0304661021         <NA>
## Q120014.fctr    -0.0318620439               0 0.0318620439         <NA>
## Q115899.fctr    -0.0324177950               0 0.0324177950         <NA>
## Q106388.fctr    -0.0341579350               0 0.0341579350 Q106272.fctr
## Q99480.fctr     -0.0344412239               0 0.0344412239         <NA>
## Q122771.fctr    -0.0348421015               0 0.0348421015         <NA>
## Q108855.fctr    -0.0370970211               0 0.0370970211         <NA>
## Q110740.fctr    -0.0380691243               0 0.0380691243         <NA>
## Q106272.fctr    -0.0400926462               0 0.0400926462         <NA>
## Q101596.fctr    -0.0409784077               0 0.0409784077         <NA>
## Q116881.fctr    -0.0416860293               0 0.0416860293         <NA>
## Q120472.fctr    -0.0462030674               0 0.0462030674         <NA>
## Q98197.fctr     -0.0549342527               0 0.0549342527         <NA>
## Q113181.fctr    -0.0808753072               0 0.0808753072         <NA>
## Q115611.fctr    -0.0904468203               0 0.0904468203         <NA>
## Gender.fctr     -0.1027400851               0 0.1027400851         <NA>
##                 freqRatio percentUnique zeroVar   nzv is.cor.y.abs.low
## Q109244.fctr     1.125916    0.05387931   FALSE FALSE            FALSE
## .clusterid       1.784041    0.07183908   FALSE FALSE            FALSE
## .clusterid.fctr  1.784041    0.07183908   FALSE FALSE            FALSE
## Hhold.fctr       1.525094    0.12571839   FALSE FALSE            FALSE
## Edn.fctr         1.392610    0.14367816   FALSE FALSE            FALSE
## Q101163.fctr     1.327394    0.05387931   FALSE FALSE            FALSE
## Q100689.fctr     1.029800    0.05387931   FALSE FALSE            FALSE
## Q98078.fctr      1.266595    0.05387931   FALSE FALSE            FALSE
## Q99716.fctr      1.328693    0.05387931   FALSE FALSE            FALSE
## Q120379.fctr     1.046326    0.05387931   FALSE FALSE            FALSE
## Q121699.fctr     1.507127    0.05387931   FALSE FALSE            FALSE
## Q105840.fctr     1.275362    0.05387931   FALSE FALSE            FALSE
## Q113583.fctr     1.102515    0.05387931   FALSE FALSE            FALSE
## Q115195.fctr     1.065496    0.05387931   FALSE FALSE            FALSE
## Q102089.fctr     1.055963    0.05387931   FALSE FALSE            FALSE
## Q98059.fctr      1.493810    0.05387931   FALSE FALSE            FALSE
## Q114386.fctr     1.092072    0.05387931   FALSE FALSE            FALSE
## Q100680.fctr     1.102386    0.05387931   FALSE FALSE            FALSE
## Q108342.fctr     1.048292    0.05387931   FALSE FALSE            FALSE
## Q111848.fctr     1.113602    0.05387931   FALSE FALSE            FALSE
## YOB.Age.fctr     1.005794    0.16163793   FALSE FALSE            FALSE
## Q118892.fctr     1.347380    0.05387931   FALSE FALSE            FALSE
## Q102687.fctr     1.256545    0.05387931   FALSE FALSE            FALSE
## Q115390.fctr     1.150505    0.05387931   FALSE FALSE            FALSE
## Q119851.fctr     1.244519    0.05387931   FALSE FALSE            FALSE
## Q114517.fctr     1.183374    0.05387931   FALSE FALSE            FALSE
## Q120012.fctr     1.047185    0.05387931   FALSE FALSE            FALSE
## Q109367.fctr     1.008571    0.05387931   FALSE FALSE            FALSE
## Q114961.fctr     1.250436    0.05387931   FALSE FALSE            FALSE
## Q121700.fctr     1.708221    0.05387931   FALSE FALSE             TRUE
## Q124122.fctr     1.412807    0.05387931   FALSE FALSE             TRUE
## Q111220.fctr     1.262849    0.05387931   FALSE FALSE             TRUE
## Q113992.fctr     1.267442    0.05387931   FALSE FALSE             TRUE
## Q121011.fctr     1.153676    0.05387931   FALSE FALSE             TRUE
## Q106042.fctr     1.247738    0.05387931   FALSE FALSE             TRUE
## Q116448.fctr     1.161031    0.05387931   FALSE FALSE             TRUE
## Q116601.fctr     1.394914    0.05387931   FALSE FALSE             TRUE
## Q104996.fctr     1.173840    0.05387931   FALSE FALSE             TRUE
## Q102906.fctr     1.053396    0.05387931   FALSE FALSE             TRUE
## Q113584.fctr     1.212486    0.05387931   FALSE FALSE             TRUE
## Q108950.fctr     1.103872    0.05387931   FALSE FALSE             TRUE
## Q102674.fctr     1.073412    0.05387931   FALSE FALSE             TRUE
## Q103293.fctr     1.122287    0.05387931   FALSE FALSE             TRUE
## Q112478.fctr     1.113648    0.05387931   FALSE FALSE             TRUE
## Q114748.fctr     1.051125    0.05387931   FALSE FALSE             TRUE
## Q107491.fctr     1.419021    0.05387931   FALSE FALSE             TRUE
## Q100562.fctr     1.217215    0.05387931   FALSE FALSE             TRUE
## Q108617.fctr     1.390618    0.05387931   FALSE FALSE             TRUE
## Q100010.fctr     1.268156    0.05387931   FALSE FALSE             TRUE
## Q115602.fctr     1.322302    0.05387931   FALSE FALSE             TRUE
## Q116953.fctr     1.039180    0.05387931   FALSE FALSE             TRUE
## Q115610.fctr     1.359695    0.05387931   FALSE FALSE             TRUE
## Q106997.fctr     1.177632    0.05387931   FALSE FALSE             TRUE
## Q120978.fctr     1.131963    0.05387931   FALSE FALSE             TRUE
## Q112512.fctr     1.299253    0.05387931   FALSE FALSE             TRUE
## Q108343.fctr     1.064910    0.05387931   FALSE FALSE             TRUE
## Q96024.fctr      1.144428    0.05387931   FALSE FALSE             TRUE
## Q106389.fctr     1.341307    0.05387931   FALSE FALSE             TRUE
## .rnorm           1.000000  100.00000000   FALSE FALSE            FALSE
## Q108754.fctr     1.008090    0.05387931   FALSE FALSE            FALSE
## Q98578.fctr      1.093556    0.05387931   FALSE FALSE            FALSE
## Q101162.fctr     1.103229    0.05387931   FALSE FALSE            FALSE
## Q115777.fctr     1.140288    0.05387931   FALSE FALSE            FALSE
## Q99581.fctr      1.375000    0.05387931   FALSE FALSE            FALSE
## Q124742.fctr     2.565379    0.05387931   FALSE FALSE            FALSE
## Q116797.fctr     1.009589    0.05387931   FALSE FALSE            FALSE
## Q112270.fctr     1.254284    0.05387931   FALSE FALSE            FALSE
## YOB              1.027559    1.41882184   FALSE FALSE            FALSE
## Q118237.fctr     1.088017    0.05387931   FALSE FALSE            FALSE
## Q119650.fctr     1.456978    0.05387931   FALSE FALSE            FALSE
## Q111580.fctr     1.024977    0.05387931   FALSE FALSE            FALSE
## Q123464.fctr     1.326681    0.05387931   FALSE FALSE            FALSE
## Q117193.fctr     1.140665    0.05387931   FALSE FALSE            FALSE
## Q99982.fctr      1.339380    0.05387931   FALSE FALSE            FALSE
## Q108856.fctr     1.080645    0.05387931   FALSE FALSE            FALSE
## Q118233.fctr     1.199142    0.05387931   FALSE FALSE            FALSE
## Q102289.fctr     1.033482    0.05387931   FALSE FALSE            FALSE
## Q116197.fctr     1.073778    0.05387931   FALSE FALSE            FALSE
## Income.fctr      1.256724    0.12571839   FALSE FALSE            FALSE
## Q118232.fctr     1.365812    0.05387931   FALSE FALSE            FALSE
## Q120194.fctr     1.016716    0.05387931   FALSE FALSE            FALSE
## Q114152.fctr     1.027617    0.05387931   FALSE FALSE            FALSE
## Q122770.fctr     1.008802    0.05387931   FALSE FALSE            FALSE
## Q117186.fctr     1.053878    0.05387931   FALSE FALSE            FALSE
## Q105655.fctr     1.079316    0.05387931   FALSE FALSE            FALSE
## Q106993.fctr     1.327392    0.05387931   FALSE FALSE            FALSE
## Q119334.fctr     1.081498    0.05387931   FALSE FALSE            FALSE
## Q122120.fctr     1.297443    0.05387931   FALSE FALSE            FALSE
## Q116441.fctr     1.019645    0.05387931   FALSE FALSE            FALSE
## Q118117.fctr     1.174006    0.05387931   FALSE FALSE            FALSE
## Q123621.fctr     1.466381    0.05387931   FALSE FALSE            FALSE
## Q122769.fctr     1.060606    0.05387931   FALSE FALSE            FALSE
## Q120650.fctr     1.896247    0.05387931   FALSE FALSE            FALSE
## Q98869.fctr      1.080860    0.05387931   FALSE FALSE            FALSE
## .pos             1.000000  100.00000000   FALSE FALSE            FALSE
## USER_ID          1.000000  100.00000000   FALSE FALSE            FALSE
## Q107869.fctr     1.211050    0.05387931   FALSE FALSE            FALSE
## Q120014.fctr     1.044944    0.05387931   FALSE FALSE            FALSE
## Q115899.fctr     1.197849    0.05387931   FALSE FALSE            FALSE
## Q106388.fctr     1.065033    0.05387931   FALSE FALSE            FALSE
## Q99480.fctr      1.225404    0.05387931   FALSE FALSE            FALSE
## Q122771.fctr     1.414753    0.05387931   FALSE FALSE            FALSE
## Q108855.fctr     1.273980    0.05387931   FALSE FALSE            FALSE
## Q110740.fctr     1.050779    0.05387931   FALSE FALSE            FALSE
## Q106272.fctr     1.116536    0.05387931   FALSE FALSE            FALSE
## Q101596.fctr     1.041667    0.05387931   FALSE FALSE            FALSE
## Q116881.fctr     1.010066    0.05387931   FALSE FALSE            FALSE
## Q120472.fctr     1.292633    0.05387931   FALSE FALSE            FALSE
## Q98197.fctr      1.129371    0.05387931   FALSE FALSE            FALSE
## Q113181.fctr     1.006354    0.05387931   FALSE FALSE            FALSE
## Q115611.fctr     1.194859    0.05387931   FALSE FALSE            FALSE
## Gender.fctr      1.561033    0.05387931   FALSE FALSE            FALSE

## Warning in myplot_scatter(plt_feats_df, "percentUnique", "freqRatio",
## colorcol_name = "nzv", : converting nzv to class:factor

## Warning: Removed 3 rows containing missing values (geom_point).

## Warning: Removed 3 rows containing missing values (geom_point).

## Warning: Removed 3 rows containing missing values (geom_point).

## [1] cor.y            exclude.as.feat  cor.y.abs        cor.high.X      
## [5] freqRatio        percentUnique    zeroVar          nzv             
## [9] is.cor.y.abs.low
## <0 rows> (or 0-length row.names)

## Scale for 'y' is already present. Adding another scale for 'y', which
## will replace the existing scale.

## [1] "numeric data missing in glbObsAll: "
##        YOB Party.fctr 
##        415       1392 
## [1] "numeric data w/ 0s in glbObsAll: "
## named integer(0)
## [1] "numeric data w/ Infs in glbObsAll: "
## named integer(0)
## [1] "numeric data w/ NaNs in glbObsAll: "
## named integer(0)
## [1] "string data missing in glbObsAll: "
##          Gender          Income HouseholdStatus  EducationLevel 
##             143            1273             552            1067 
##           Party         Q124742         Q124122         Q123464 
##              NA            4340            3114            2912 
##         Q123621         Q122769         Q122770         Q122771 
##            3018            2778            2597            2579 
##         Q122120         Q121699         Q121700         Q120978 
##            2552            2279            2328            2303 
##         Q121011         Q120379         Q120650         Q120472 
##            2256            2361            2283            2433 
##         Q120194         Q120012         Q120014         Q119334 
##            2603            2344            2571            2477 
##         Q119851         Q119650         Q118892         Q118117 
##            2243            2374            2206            2342 
##         Q118232         Q118233         Q118237         Q117186 
##            3018            2659            2592            2845 
##         Q117193         Q116797         Q116881         Q116953 
##            2799            2771            2889            2848 
##         Q116601         Q116441         Q116448         Q116197 
##            2606            2684            2730            2657 
##         Q115602         Q115777         Q115610         Q115611 
##            2619            2785            2637            2443 
##         Q115899         Q115390         Q114961         Q114748 
##            2789            2860            2687            2462 
##         Q115195         Q114517         Q114386         Q113992 
##            2647            2567            2686            2502 
##         Q114152         Q113583         Q113584         Q113181 
##            2829            2632            2654            2576 
##         Q112478         Q112512         Q112270         Q111848 
##            2790            2676            2820            2449 
##         Q111580         Q111220         Q110740         Q109367 
##            2686            2563            2479            2624 
##         Q108950         Q109244         Q108855         Q108617 
##            2641            2731            3008            2696 
##         Q108856         Q108754         Q108342         Q108343 
##            3007            2770            2760            2736 
##         Q107869         Q107491         Q106993         Q106997 
##            2762            2667            2676            2702 
##         Q106272         Q106388         Q106389         Q106042 
##            2722            2818            2871            2762 
##         Q105840         Q105655         Q104996         Q103293 
##            2876            2612            2620            2674 
##         Q102906         Q102674         Q102687         Q102289 
##            2840            2864            2712            2790 
##         Q102089         Q101162         Q101163         Q101596 
##            2736            2816            2995            2824 
##         Q100689         Q100680         Q100562          Q99982 
##            2568            2787            2793            2871 
##         Q100010          Q99716          Q99581          Q99480 
##            2688            2790            2690            2700 
##          Q98869          Q98578          Q98059          Q98078 
##            2906            2867            2629            2945 
##          Q98197          Q96024            .lcn 
##            2836            2858            1392

## [1] "glb_feats_df:"

## [1] 112  12

##                    id exclude.as.feat rsp_var
## Party.fctr Party.fctr            TRUE    TRUE

##                    id       cor.y exclude.as.feat  cor.y.abs cor.high.X
## USER_ID       USER_ID -0.03023049            TRUE 0.03023049       <NA>
## Party.fctr Party.fctr          NA            TRUE         NA       <NA>
##            freqRatio percentUnique zeroVar   nzv is.cor.y.abs.low
## USER_ID            1           100   FALSE FALSE            FALSE
## Party.fctr        NA            NA      NA    NA               NA
##            interaction.feat shapiro.test.p.value rsp_var_raw id_var
## USER_ID                <NA>                   NA       FALSE   TRUE
## Party.fctr             <NA>                   NA          NA     NA
##            rsp_var
## USER_ID         NA
## Party.fctr    TRUE

## [1] "glb_feats_df vs. glbObsAll: "

## character(0)

## [1] "glbObsAll vs. glb_feats_df: "

## character(0)

##             label step_major step_minor label_minor     bgn     end
## 3 select.features          3          0           0 253.954 259.958
## 4      fit.models          4          0           0 259.959      NA
##   elapsed
## 3   6.005
## 4      NA

Step 4.0: fit models

fit.models_0_chunk_df <- myadd_chunk(NULL, "fit.models_0_bgn", label.minor = "setup")

##              label step_major step_minor label_minor     bgn end elapsed
## 1 fit.models_0_bgn          1          0       setup 260.534  NA      NA

# load(paste0(glbOut$pfx, "dsk.RData"))

get_model_sel_frmla <- function() {
    model_evl_terms <- c(NULL)
    # min.aic.fit might not be avl
    lclMdlEvlCriteria <- 
        glbMdlMetricsEval[glbMdlMetricsEval %in% names(glb_models_df)]
    for (metric in lclMdlEvlCriteria)
        model_evl_terms <- c(model_evl_terms, 
                             ifelse(length(grep("max", metric)) > 0, "-", "+"), metric)
    if (glb_is_classification && glb_is_binomial)
        model_evl_terms <- c(model_evl_terms, "-", "opt.prob.threshold.OOB")
    model_sel_frmla <- as.formula(paste(c("~ ", model_evl_terms), collapse = " "))
    return(model_sel_frmla)
}

get_dsp_models_df <- function() {
    dsp_models_cols <- c("id", 
                    glbMdlMetricsEval[glbMdlMetricsEval %in% names(glb_models_df)],
                    grep("opt.", names(glb_models_df), fixed = TRUE, value = TRUE)) 
    dsp_models_df <- 
        #orderBy(get_model_sel_frmla(), glb_models_df)[, c("id", glbMdlMetricsEval)]
        orderBy(get_model_sel_frmla(), glb_models_df)[, dsp_models_cols]    
    nCvMdl <- sapply(glb_models_lst, function(mdl) nrow(mdl$results))
    nParams <- sapply(glb_models_lst, function(mdl) ifelse(mdl$method == "custom", 0, 
        nrow(subset(modelLookup(mdl$method), parameter != "parameter"))))
    
#     nCvMdl <- nCvMdl[names(nCvMdl) != "avNNet"]
#     nParams <- nParams[names(nParams) != "avNNet"]    
    
    if (length(cvMdlProblems <- nCvMdl[nCvMdl <= nParams]) > 0) {
        print("Cross Validation issues:")
        warning("Cross Validation issues:")        
        print(cvMdlProblems)
    }
    
    pltMdls <- setdiff(names(nCvMdl), names(cvMdlProblems))
    pltMdls <- setdiff(pltMdls, names(nParams[nParams == 0]))
    
    # length(pltMdls) == 21
    png(paste0(glbOut$pfx, "bestTune.png"), width = 480 * 2, height = 480 * 4)
    grid.newpage()
    pushViewport(viewport(layout = grid.layout(ceiling(length(pltMdls) / 2.0), 2)))
    pltIx <- 1
    for (mdlId in pltMdls) {
        print(ggplot(glb_models_lst[[mdlId]], highBestTune = TRUE) + labs(title = mdlId),   
              vp = viewport(layout.pos.row = ceiling(pltIx / 2.0), 
                            layout.pos.col = ((pltIx - 1) %% 2) + 1))  
        pltIx <- pltIx + 1
    }
    dev.off()

    if (all(row.names(dsp_models_df) != dsp_models_df$id))
        row.names(dsp_models_df) <- dsp_models_df$id
    return(dsp_models_df)
}
#get_dsp_models_df()

if (glb_is_classification && glb_is_binomial && 
        (length(unique(glbObsFit[, glb_rsp_var])) < 2))
    stop("glbObsFit$", glb_rsp_var, ": contains less than 2 unique values: ",
         paste0(unique(glbObsFit[, glb_rsp_var]), collapse=", "))

max_cor_y_x_vars <- orderBy(~ -cor.y.abs, 
        subset(glb_feats_df, (exclude.as.feat == 0) & !nzv & !is.cor.y.abs.low & 
                                is.na(cor.high.X)))[1:2, "id"]
max_cor_y_x_vars <- max_cor_y_x_vars[!is.na(max_cor_y_x_vars)]
if (length(max_cor_y_x_vars) < 2)
    max_cor_y_x_vars <- union(max_cor_y_x_vars, ".pos")

if (!is.null(glb_Baseline_mdl_var)) {
    if ((max_cor_y_x_vars[1] != glb_Baseline_mdl_var) & 
        (glb_feats_df[glb_feats_df$id == max_cor_y_x_vars[1], "cor.y.abs"] > 
         glb_feats_df[glb_feats_df$id == glb_Baseline_mdl_var, "cor.y.abs"]))
        stop(max_cor_y_x_vars[1], " has a higher correlation with ", glb_rsp_var, 
             " than the Baseline var: ", glb_Baseline_mdl_var)
}

glb_model_type <- ifelse(glb_is_regression, "regression", "classification")
    
# Model specs
# c("id.prefix", "method", "type",
#   # trainControl params
#   "preProc.method", "cv.n.folds", "cv.n.repeats", "summary.fn",
#   # train params
#   "metric", "metric.maximize", "tune.df")

# Baseline
if (!is.null(glb_Baseline_mdl_var)) {
    fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df, 
                            paste0("fit.models_0_", "Baseline"), major.inc = FALSE,
                                    label.minor = "mybaseln_classfr")
    ret_lst <- myfit_mdl(mdl_id="Baseline", 
                         model_method="mybaseln_classfr",
                        indepVar=glb_Baseline_mdl_var,
                        rsp_var=glb_rsp_var,
                        fit_df=glbObsFit, OOB_df=glbObsOOB)
}    

# Most Frequent Outcome "MFO" model: mean(y) for regression
#   Not using caret's nullModel since model stats not avl
#   Cannot use rpart for multinomial classification since it predicts non-MFO
if (glb_is_classification) {
    fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df, 
                                paste0("fit.models_0_", "MFO"), major.inc = FALSE,
                                        label.minor = "myMFO_classfr")

    ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
        id.prefix = "MFO", type = glb_model_type, trainControl.method = "none",
        train.method = ifelse(glb_is_regression, "lm", "myMFO_classfr"))),
                            indepVar = ".rnorm", rsp_var = glb_rsp_var,
                            fit_df = glbObsFit, OOB_df = glbObsOOB)

        # "random" model - only for classification; 
        #   none needed for regression since it is same as MFO
    fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df, 
                                paste0("fit.models_0_", "Random"), major.inc = FALSE,
                                        label.minor = "myrandom_classfr")

#stop(here"); glb2Sav(); all.equal(glb_models_df, sav_models_df)    
    ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
        id.prefix = "Random", type = glb_model_type, trainControl.method = "none",
        train.method = "myrandom_classfr")),
                        indepVar = ".rnorm", rsp_var = glb_rsp_var,
                        fit_df = glbObsFit, OOB_df = glbObsOOB)
}

##              label step_major step_minor   label_minor     bgn     end
## 1 fit.models_0_bgn          1          0         setup 260.534 260.567
## 2 fit.models_0_MFO          1          1 myMFO_classfr 260.568      NA
##   elapsed
## 1   0.033
## 2      NA
## [1] "myfit_mdl: enter: 0.000000 secs"
## [1] "fitting model: MFO###myMFO_classfr"
## [1] "    indepVar: .rnorm"
## [1] "myfit_mdl: setup complete: 0.469000 secs"
## Fitting parameter = none on full training set
## [1] "in MFO.Classifier$fit"
## [1] "unique.vals:"
## [1] R D
## Levels: R D
## [1] "unique.prob:"
## y
##         D         R 
## 0.5299011 0.4700989 
## [1] "MFO.val:"
## [1] "D"
## [1] "myfit_mdl: train complete: 1.109000 secs"
##             Length Class      Mode     
## unique.vals 2      factor     numeric  
## unique.prob 2      -none-     numeric  
## MFO.val     1      -none-     character
## x.names     1      -none-     character
## xNames      1      -none-     character
## problemType 1      -none-     character
## tuneValue   1      data.frame list     
## obsLevels   2      -none-     character
## [1] "myfit_mdl: train diagnostics complete: 1.111000 secs"

## Loading required namespace: pROC

## [1] "entr MFO.Classifier$predict"
## [1] "exit MFO.Classifier$predict"

## Loading required package: ROCR

## Loading required package: gplots

## 
## Attaching package: 'gplots'

## The following object is masked from 'package:stats':
## 
##     lowess

## [1] "in MFO.Classifier$prob"
##           R         D
## 1 0.5299011 0.4700989
## 2 0.5299011 0.4700989
## 3 0.5299011 0.4700989
## 4 0.5299011 0.4700989
## 5 0.5299011 0.4700989
## 6 0.5299011 0.4700989

##          Prediction
## Reference    R    D
##         R 2091    0
##         D 2357    0
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##      0.4700989      0.0000000      0.4553427      0.4848945      0.5299011 
## AccuracyPValue  McnemarPValue 
##      1.0000000      0.0000000 
## [1] "entr MFO.Classifier$predict"
## [1] "exit MFO.Classifier$predict"
## [1] "in MFO.Classifier$prob"
##           R         D
## 1 0.5299011 0.4700989
## 2 0.5299011 0.4700989
## 3 0.5299011 0.4700989
## 4 0.5299011 0.4700989
## 5 0.5299011 0.4700989
## 6 0.5299011 0.4700989

##          Prediction
## Reference   R   D
##         R 526   0
##         D 594   0
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   4.696429e-01   0.000000e+00   4.400805e-01   4.993651e-01   5.303571e-01 
## AccuracyPValue  McnemarPValue 
##   9.999790e-01  9.194240e-131 
## [1] "myfit_mdl: predict complete: 6.060000 secs"
##                    id  feats max.nTuningRuns min.elapsedtime.everything
## 1 MFO###myMFO_classfr .rnorm               0                      0.628
##   min.elapsedtime.final max.AUCpROC.fit max.Sens.fit max.Spec.fit
## 1                 0.004             0.5            0            1
##   max.AUCROCR.fit opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1             0.5                    0.5       0.6395473        0.4700989
##   max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1             0.4553427             0.4848945             0
##   max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1             0.5            0            1             0.5
##   opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1                    0.5       0.6391252        0.4696429
##   max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1             0.4400805             0.4993651             0
## [1] "myfit_mdl: exit: 6.070000 secs"
##                 label step_major step_minor      label_minor     bgn
## 2    fit.models_0_MFO          1          1    myMFO_classfr 260.568
## 3 fit.models_0_Random          1          2 myrandom_classfr 266.643
##       end elapsed
## 2 266.643   6.075
## 3      NA      NA
## [1] "myfit_mdl: enter: 0.000000 secs"
## [1] "fitting model: Random###myrandom_classfr"
## [1] "    indepVar: .rnorm"
## [1] "myfit_mdl: setup complete: 0.437000 secs"
## Fitting parameter = none on full training set
## [1] "myfit_mdl: train complete: 0.736000 secs"
##             Length Class      Mode     
## unique.vals 2      factor     numeric  
## unique.prob 2      table      numeric  
## xNames      1      -none-     character
## problemType 1      -none-     character
## tuneValue   1      data.frame list     
## obsLevels   2      -none-     character
## [1] "myfit_mdl: train diagnostics complete: 0.737000 secs"
## [1] "in Random.Classifier$prob"

##          Prediction
## Reference    R    D
##         R 2091    0
##         D 2357    0
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##      0.4700989      0.0000000      0.4553427      0.4848945      0.5299011 
## AccuracyPValue  McnemarPValue 
##      1.0000000      0.0000000 
## [1] "in Random.Classifier$prob"

##          Prediction
## Reference   R   D
##         R 526   0
##         D 594   0
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   4.696429e-01   0.000000e+00   4.400805e-01   4.993651e-01   5.303571e-01 
## AccuracyPValue  McnemarPValue 
##   9.999790e-01  9.194240e-131 
## [1] "myfit_mdl: predict complete: 6.814000 secs"
##                          id  feats max.nTuningRuns
## 1 Random###myrandom_classfr .rnorm               0
##   min.elapsedtime.everything min.elapsedtime.final max.AUCpROC.fit
## 1                      0.292                 0.003       0.4942483
##   max.Sens.fit max.Spec.fit max.AUCROCR.fit opt.prob.threshold.fit
## 1    0.4619799    0.5265168       0.5073101                   0.55
##   max.f.score.fit max.Accuracy.fit max.AccuracyLower.fit
## 1       0.6395473        0.4700989             0.4553427
##   max.AccuracyUpper.fit max.Kappa.fit max.AUCpROC.OOB max.Sens.OOB
## 1             0.4848945             0        0.523569          0.5
##   max.Spec.OOB max.AUCROCR.OOB opt.prob.threshold.OOB max.f.score.OOB
## 1     0.547138       0.5191202                   0.55       0.6391252
##   max.Accuracy.OOB max.AccuracyLower.OOB max.AccuracyUpper.OOB
## 1        0.4696429             0.4400805             0.4993651
##   max.Kappa.OOB
## 1             0
## [1] "myfit_mdl: exit: 6.826000 secs"

# Max.cor.Y
#   Check impact of cv
#       rpart is not a good candidate since caret does not optimize cp (only tuning parameter of rpart) well
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df, 
                        paste0("fit.models_0_", "Max.cor.Y.rcv.*X*"), major.inc = FALSE,
                                    label.minor = "glmnet")

##                            label step_major step_minor      label_minor
## 3            fit.models_0_Random          1          2 myrandom_classfr
## 4 fit.models_0_Max.cor.Y.rcv.*X*          1          3           glmnet
##       bgn     end elapsed
## 3 266.643 273.481   6.838
## 4 273.481      NA      NA

ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
    id.prefix = "Max.cor.Y.rcv.1X1", type = glb_model_type, trainControl.method = "none",
    train.method = "glmnet")),
                    indepVar = max_cor_y_x_vars, rsp_var = glb_rsp_var, 
                    fit_df = glbObsFit, OOB_df = glbObsOOB)

## [1] "myfit_mdl: enter: 0.000000 secs"
## [1] "fitting model: Max.cor.Y.rcv.1X1###glmnet"
## [1] "    indepVar: Q109244.fctr,Gender.fctr"
## [1] "myfit_mdl: setup complete: 0.692000 secs"

## Loading required package: glmnet

## Loading required package: Matrix

## 
## Attaching package: 'Matrix'

## The following object is masked from 'package:tidyr':
## 
##     expand

## Loaded glmnet 2.0-5

## Fitting alpha = 0.1, lambda = 0.00248 on full training set
## [1] "myfit_mdl: train complete: 1.593000 secs"

##             Length Class      Mode     
## a0           58    -none-     numeric  
## beta        232    dgCMatrix  S4       
## df           58    -none-     numeric  
## dim           2    -none-     numeric  
## lambda       58    -none-     numeric  
## dev.ratio    58    -none-     numeric  
## nulldev       1    -none-     numeric  
## npasses       1    -none-     numeric  
## jerr          1    -none-     numeric  
## offset        1    -none-     logical  
## classnames    2    -none-     character
## call          5    -none-     call     
## nobs          1    -none-     numeric  
## lambdaOpt     1    -none-     numeric  
## xNames        4    -none-     character
## problemType   1    -none-     character
## tuneValue     2    data.frame list     
## obsLevels     2    -none-     character
## [1] "min lambda > lambdaOpt:"
##     (Intercept)    Gender.fctrM  Q109244.fctrNo Q109244.fctrYes 
##       0.2665753      -0.2101506      -0.4308362       1.2139586 
## [1] "max lambda < lambdaOpt:"
## [1] "Feats mismatch between coefs_left & rght:"
## [1] "(Intercept)"     "Gender.fctrF"    "Gender.fctrM"    "Q109244.fctrNo" 
## [5] "Q109244.fctrYes"
## [1] "myfit_mdl: train diagnostics complete: 1.696000 secs"

##          Prediction
## Reference    R    D
##         R 1950  141
##         D 1762  595
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   5.721673e-01   1.772539e-01   5.574714e-01   5.867683e-01   5.299011e-01 
## AccuracyPValue  McnemarPValue 
##   8.241814e-09  7.365212e-302

##          Prediction
## Reference   R   D
##         R 484  42
##         D 447 147
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   5.633929e-01   1.605510e-01   5.337655e-01   5.926864e-01   5.303571e-01 
## AccuracyPValue  McnemarPValue 
##   1.432605e-02   1.447405e-74 
## [1] "myfit_mdl: predict complete: 9.221000 secs"
##                           id                    feats max.nTuningRuns
## 1 Max.cor.Y.rcv.1X1###glmnet Q109244.fctr,Gender.fctr               0
##   min.elapsedtime.everything min.elapsedtime.final max.AUCpROC.fit
## 1                      0.892                 0.064       0.5971118
##   max.Sens.fit max.Spec.fit max.AUCROCR.fit opt.prob.threshold.fit
## 1    0.5480631    0.6461604       0.3580613                    0.6
##   max.f.score.fit max.Accuracy.fit max.AccuracyLower.fit
## 1       0.6720662        0.5721673             0.5574714
##   max.AccuracyUpper.fit max.Kappa.fit max.AUCpROC.OOB max.Sens.OOB
## 1             0.5867683     0.1772539       0.5896897    0.5228137
##   max.Spec.OOB max.AUCROCR.OOB opt.prob.threshold.OOB max.f.score.OOB
## 1    0.6565657       0.3658672                    0.6       0.6643789
##   max.Accuracy.OOB max.AccuracyLower.OOB max.AccuracyUpper.OOB
## 1        0.5633929             0.5337655             0.5926864
##   max.Kappa.OOB
## 1      0.160551
## [1] "myfit_mdl: exit: 9.234000 secs"

if (glbMdlCheckRcv) {
    # rcv_n_folds == 1 & rcv_n_repeats > 1 crashes
    for (rcv_n_folds in seq(3, glb_rcv_n_folds + 2, 2))
        for (rcv_n_repeats in seq(1, glb_rcv_n_repeats + 2, 2)) {
            
            # Experiment specific code to avoid caret crash
    #         lcl_tune_models_df <- rbind(data.frame()
    #                             ,data.frame(method = "glmnet", parameter = "alpha", 
    #                                         vals = "0.100 0.325 0.550 0.775 1.000")
    #                             ,data.frame(method = "glmnet", parameter = "lambda",
    #                                         vals = "9.342e-02")    
    #                                     )
            
            ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst =
                list(
                id.prefix = paste0("Max.cor.Y.rcv.", rcv_n_folds, "X", rcv_n_repeats), 
                type = glb_model_type, 
    # tune.df = lcl_tune_models_df,            
                trainControl.method = "repeatedcv",
                trainControl.number = rcv_n_folds, 
                trainControl.repeats = rcv_n_repeats,
                trainControl.classProbs = glb_is_classification,
                trainControl.summaryFunction = glbMdlMetricSummaryFn,
                train.method = "glmnet", train.metric = glbMdlMetricSummary, 
                train.maximize = glbMdlMetricMaximize)),
                                indepVar = max_cor_y_x_vars, rsp_var = glb_rsp_var, 
                                fit_df = glbObsFit, OOB_df = glbObsOOB)
        }
    # Add parallel coordinates graph of glb_models_df[, glbMdlMetricsEval] to evaluate cv parameters
    tmp_models_cols <- c("id", "max.nTuningRuns",
                        glbMdlMetricsEval[glbMdlMetricsEval %in% names(glb_models_df)],
                        grep("opt.", names(glb_models_df), fixed = TRUE, value = TRUE)) 
    print(myplot_parcoord(obs_df = subset(glb_models_df, 
                                          grepl("Max.cor.Y.rcv.", id, fixed = TRUE), 
                                            select = -feats)[, tmp_models_cols],
                          id_var = "id"))
}
        
# Useful for stacking decisions
# fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df, 
#                     paste0("fit.models_0_", "Max.cor.Y[rcv.1X1.cp.0|]"), major.inc = FALSE,
#                                     label.minor = "rpart")
# 
# ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
#     id.prefix = "Max.cor.Y.rcv.1X1.cp.0", type = glb_model_type, trainControl.method = "none",
#     train.method = "rpart",
#     tune.df=data.frame(method="rpart", parameter="cp", min=0.0, max=0.0, by=0.1))),
#                     indepVar=max_cor_y_x_vars, rsp_var=glb_rsp_var, 
#                     fit_df=glbObsFit, OOB_df=glbObsOOB)

#stop(here"); glb2Sav(); all.equal(glb_models_df, sav_models_df)
# if (glb_is_regression || glb_is_binomial) # For multinomials this model will be run next by default
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
                        id.prefix = "Max.cor.Y", 
                        type = glb_model_type, trainControl.method = "repeatedcv",
                        trainControl.number = glb_rcv_n_folds, 
                        trainControl.repeats = glb_rcv_n_repeats,
                        trainControl.classProbs = glb_is_classification,
                        trainControl.summaryFunction = glbMdlMetricSummaryFn,
                        trainControl.allowParallel = glbMdlAllowParallel,                        
                        train.metric = glbMdlMetricSummary, 
                        train.maximize = glbMdlMetricMaximize,    
                        train.method = "rpart")),
                    indepVar = max_cor_y_x_vars, rsp_var = glb_rsp_var, 
                    fit_df = glbObsFit, OOB_df = glbObsOOB)

## [1] "myfit_mdl: enter: 0.000000 secs"
## [1] "fitting model: Max.cor.Y##rcv#rpart"
## [1] "    indepVar: Q109244.fctr,Gender.fctr"
## [1] "myfit_mdl: setup complete: 0.730000 secs"

## Loading required package: rpart

## Aggregating results
## Selecting tuning parameters
## Fitting cp = 0.0225 on full training set
## [1] "myfit_mdl: train complete: 2.931000 secs"

## Loading required package: rpart.plot

## Call:
## rpart(formula = .outcome ~ ., control = list(minsplit = 20, minbucket = 7, 
##     cp = 0, maxcompete = 4, maxsurrogate = 5, usesurrogate = 2, 
##     surrogatestyle = 0, maxdepth = 30, xval = 0))
##   n= 4448 
## 
##           CP nsplit rel error
## 1 0.08990913      0 1.0000000
## 2 0.05930177      1 0.9100909
## 3 0.02247728      2 0.8507891
## 
## Variable importance
## Q109244.fctrYes  Q109244.fctrNo    Gender.fctrM    Gender.fctrF 
##              83              15               1               1 
## 
## Node number 1: 4448 observations,    complexity param=0.08990913
##   predicted class=D  expected loss=0.4700989  P(node) =1
##     class counts:  2091  2357
##    probabilities: 0.470 0.530 
##   left son=2 (3712 obs) right son=3 (736 obs)
##   Primary splits:
##       Q109244.fctrYes < 0.5 to the left,  improve=136.83150, (0 missing)
##       Q109244.fctrNo  < 0.5 to the right, improve= 84.31128, (0 missing)
##       Gender.fctrM    < 0.5 to the right, improve= 24.39999, (0 missing)
##       Gender.fctrF    < 0.5 to the left,  improve= 22.65952, (0 missing)
## 
## Node number 2: 3712 observations,    complexity param=0.05930177
##   predicted class=R  expected loss=0.4746767  P(node) =0.8345324
##     class counts:  1950  1762
##    probabilities: 0.525 0.475 
##   left son=4 (1980 obs) right son=5 (1732 obs)
##   Primary splits:
##       Q109244.fctrNo < 0.5 to the right, improve=24.259840, (0 missing)
##       Gender.fctrM   < 0.5 to the right, improve=10.189980, (0 missing)
##       Gender.fctrF   < 0.5 to the left,  improve= 8.193561, (0 missing)
##   Surrogate splits:
##       Gender.fctrM < 0.5 to the right, agree=0.571, adj=0.080, (0 split)
##       Gender.fctrF < 0.5 to the left,  agree=0.563, adj=0.063, (0 split)
## 
## Node number 3: 736 observations
##   predicted class=D  expected loss=0.1915761  P(node) =0.1654676
##     class counts:   141   595
##    probabilities: 0.192 0.808 
## 
## Node number 4: 1980 observations
##   predicted class=R  expected loss=0.4212121  P(node) =0.4451439
##     class counts:  1146   834
##    probabilities: 0.579 0.421 
## 
## Node number 5: 1732 observations
##   predicted class=D  expected loss=0.4642032  P(node) =0.3893885
##     class counts:   804   928
##    probabilities: 0.464 0.536 
## 
## n= 4448 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 4448 2091 D (0.4700989 0.5299011)  
##   2) Q109244.fctrYes< 0.5 3712 1762 R (0.5253233 0.4746767)  
##     4) Q109244.fctrNo>=0.5 1980  834 R (0.5787879 0.4212121) *
##     5) Q109244.fctrNo< 0.5 1732  804 D (0.4642032 0.5357968) *
##   3) Q109244.fctrYes>=0.5 736  141 D (0.1915761 0.8084239) *
## [1] "myfit_mdl: train diagnostics complete: 4.232000 secs"

##          Prediction
## Reference    R    D
##         R 1950  141
##         D 1762  595
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   5.721673e-01   1.772539e-01   5.574714e-01   5.867683e-01   5.299011e-01 
## AccuracyPValue  McnemarPValue 
##   8.241814e-09  7.365212e-302

##          Prediction
## Reference   R   D
##         R 484  42
##         D 447 147
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   5.633929e-01   1.605510e-01   5.337655e-01   5.926864e-01   5.303571e-01 
## AccuracyPValue  McnemarPValue 
##   1.432605e-02   1.447405e-74 
## [1] "myfit_mdl: predict complete: 9.971000 secs"
##                     id                    feats max.nTuningRuns
## 1 Max.cor.Y##rcv#rpart Q109244.fctr,Gender.fctr               5
##   min.elapsedtime.everything min.elapsedtime.final max.AUCpROC.fit
## 1                      2.191                 0.025       0.5971118
##   max.Sens.fit max.Spec.fit max.AUCROCR.fit opt.prob.threshold.fit
## 1    0.5480631    0.6461604       0.3676308                   0.55
##   max.f.score.fit max.Accuracy.fit max.AccuracyLower.fit
## 1       0.6720662         0.600045             0.5574714
##   max.AccuracyUpper.fit max.Kappa.fit max.AUCpROC.OOB max.Sens.OOB
## 1             0.5867683     0.1947896       0.5896897    0.5228137
##   max.Spec.OOB max.AUCROCR.OOB opt.prob.threshold.OOB max.f.score.OOB
## 1    0.6565657       0.3774772                   0.55       0.6643789
##   max.Accuracy.OOB max.AccuracyLower.OOB max.AccuracyUpper.OOB
## 1        0.5633929             0.5337655             0.5926864
##   max.Kappa.OOB max.AccuracySD.fit max.KappaSD.fit
## 1      0.160551          0.0124035      0.02559319
## [1] "myfit_mdl: exit: 9.987000 secs"

if ((length(glbFeatsDateTime) > 0) && 
    (sum(grepl(paste(names(glbFeatsDateTime), "\\.day\\.minutes\\.poly\\.", sep = ""),
               names(glbObsAll))) > 0)) {
    fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df, 
                    paste0("fit.models_0_", "Max.cor.Y.Time.Poly"), major.inc = FALSE,
                                    label.minor = "glmnet")

    indepVars <- c(max_cor_y_x_vars, 
            grep(paste(names(glbFeatsDateTime), "\\.day\\.minutes\\.poly\\.", sep = ""),
                        names(glbObsAll), value = TRUE))
    indepVars <- myadjustInteractionFeats(glb_feats_df, indepVars)
    ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
            id.prefix = "Max.cor.Y.Time.Poly", 
            type = glb_model_type, trainControl.method = "repeatedcv",
            trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
            trainControl.classProbs = glb_is_classification,
            trainControl.summaryFunction = glbMdlMetricSummaryFn,
            trainControl.allowParallel = glbMdlAllowParallel,            
            train.metric = glbMdlMetricSummary, 
            train.maximize = glbMdlMetricMaximize,    
            train.method = "glmnet")),
        indepVar = indepVars,
        rsp_var = glb_rsp_var, 
        fit_df = glbObsFit, OOB_df = glbObsOOB)
}

if ((length(glbFeatsDateTime) > 0) && 
    (sum(grepl(paste(names(glbFeatsDateTime), "\\.last[[:digit:]]", sep = ""),
               names(glbObsAll))) > 0)) {
    fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df, 
                    paste0("fit.models_0_", "Max.cor.Y.Time.Lag"), major.inc = FALSE,
                                    label.minor = "glmnet")

    indepVars <- c(max_cor_y_x_vars, 
            grep(paste(names(glbFeatsDateTime), "\\.last[[:digit:]]", sep = ""),
                        names(glbObsAll), value = TRUE))
    indepVars <- myadjustInteractionFeats(glb_feats_df, indepVars)
    ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
        id.prefix = "Max.cor.Y.Time.Lag", 
        type = glb_model_type, 
        tune.df = glbMdlTuneParams,        
        trainControl.method = "repeatedcv",
        trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
        trainControl.classProbs = glb_is_classification,
        trainControl.summaryFunction = glbMdlMetricSummaryFn,
        trainControl.allowParallel = glbMdlAllowParallel,        
        train.metric = glbMdlMetricSummary, 
        train.maximize = glbMdlMetricMaximize,    
        train.method = "glmnet")),
        indepVar = indepVars,
        rsp_var = glb_rsp_var, 
        fit_df = glbObsFit, OOB_df = glbObsOOB)
}

if (length(glbFeatsText) > 0) {
    fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df, 
                    paste0("fit.models_0_", "Txt.*"), major.inc = FALSE,
                                    label.minor = "glmnet")

    indepVars <- c(max_cor_y_x_vars)
    for (txtFeat in names(glbFeatsText))
        indepVars <- union(indepVars, 
            grep(paste(str_to_upper(substr(txtFeat, 1, 1)), "\\.(?!([T|P]\\.))", sep = ""),
                        names(glbObsAll), perl = TRUE, value = TRUE))
    indepVars <- myadjustInteractionFeats(glb_feats_df, indepVars)
    ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
        id.prefix = "Max.cor.Y.Text.nonTP", 
        type = glb_model_type, 
        tune.df = glbMdlTuneParams,        
        trainControl.method = "repeatedcv",
        trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
        trainControl.classProbs = glb_is_classification,
        trainControl.summaryFunction = glbMdlMetricSummaryFn,
        trainControl.allowParallel = glbMdlAllowParallel,                                
        train.metric = glbMdlMetricSummary, 
        train.maximize = glbMdlMetricMaximize,    
        train.method = "glmnet")),
        indepVar = indepVars,
        rsp_var = glb_rsp_var, 
        fit_df = glbObsFit, OOB_df = glbObsOOB)

    indepVars <- c(max_cor_y_x_vars)
    for (txtFeat in names(glbFeatsText))
        indepVars <- union(indepVars, 
            grep(paste(str_to_upper(substr(txtFeat, 1, 1)), "\\.T\\.", sep = ""),
                        names(glbObsAll), perl = TRUE, value = TRUE))
    indepVars <- myadjustInteractionFeats(glb_feats_df, indepVars)
    ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
        id.prefix = "Max.cor.Y.Text.onlyT", 
        type = glb_model_type, 
        tune.df = glbMdlTuneParams,        
        trainControl.method = "repeatedcv",
        trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
        trainControl.classProbs = glb_is_classification,
        trainControl.summaryFunction = glbMdlMetricSummaryFn,
        train.metric = glbMdlMetricSummary, 
        train.maximize = glbMdlMetricMaximize,    
        train.method = "glmnet")),
        indepVar = indepVars,
        rsp_var = glb_rsp_var, 
        fit_df = glbObsFit, OOB_df = glbObsOOB)

    indepVars <- c(max_cor_y_x_vars)
    for (txtFeat in names(glbFeatsText))
        indepVars <- union(indepVars, 
            grep(paste(str_to_upper(substr(txtFeat, 1, 1)), "\\.P\\.", sep = ""),
                        names(glbObsAll), perl = TRUE, value = TRUE))
    indepVars <- myadjustInteractionFeats(glb_feats_df, indepVars)
    ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
        id.prefix = "Max.cor.Y.Text.onlyP", 
        type = glb_model_type, 
        tune.df = glbMdlTuneParams,        
        trainControl.method = "repeatedcv",
        trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
        trainControl.classProbs = glb_is_classification,
        trainControl.summaryFunction = glbMdlMetricSummaryFn,
        trainControl.allowParallel = glbMdlAllowParallel,        
        train.metric = glbMdlMetricSummary, 
        train.maximize = glbMdlMetricMaximize,    
        train.method = "glmnet")),
        indepVar = indepVars,
        rsp_var = glb_rsp_var, 
        fit_df = glbObsFit, OOB_df = glbObsOOB)
}

# Interactions.High.cor.Y
if (length(int_feats <- setdiff(setdiff(unique(glb_feats_df$cor.high.X), NA), 
                                subset(glb_feats_df, nzv)$id)) > 0) {
    fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df, 
                    paste0("fit.models_0_", "Interact.High.cor.Y"), major.inc = FALSE,
                                    label.minor = "glmnet")

    ret_lst <- myfit_mdl(mdl_specs_lst=myinit_mdl_specs_lst(mdl_specs_lst=list(
        id.prefix="Interact.High.cor.Y", 
        type=glb_model_type, trainControl.method="repeatedcv",
        trainControl.number=glb_rcv_n_folds, trainControl.repeats=glb_rcv_n_repeats,
        trainControl.classProbs = glb_is_classification,
        trainControl.summaryFunction = glbMdlMetricSummaryFn,
        trainControl.allowParallel = glbMdlAllowParallel,
        train.metric = glbMdlMetricSummary, 
        train.maximize = glbMdlMetricMaximize,    
        train.method="glmnet")),
        indepVar=c(max_cor_y_x_vars, paste(max_cor_y_x_vars[1], int_feats, sep=":")),
        rsp_var=glb_rsp_var, 
        fit_df=glbObsFit, OOB_df=glbObsOOB)
}    

##                              label step_major step_minor label_minor
## 4   fit.models_0_Max.cor.Y.rcv.*X*          1          3      glmnet
## 5 fit.models_0_Interact.High.cor.Y          1          4      glmnet
##       bgn     end elapsed
## 4 273.481 292.745  19.265
## 5 292.746      NA      NA
## [1] "myfit_mdl: enter: 0.000000 secs"
## [1] "fitting model: Interact.High.cor.Y##rcv#glmnet"
## [1] "    indepVar: Q109244.fctr,Gender.fctr,Q109244.fctr:Q99480.fctr,Q109244.fctr:Q98078.fctr,Q109244.fctr:Q100689.fctr,Q109244.fctr:Q108855.fctr,Q109244.fctr:Q123621.fctr,Q109244.fctr:Q122771.fctr,Q109244.fctr:Q120472.fctr,Q109244.fctr:Q106272.fctr"
## [1] "myfit_mdl: setup complete: 0.771000 secs"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 1, lambda = 0.00248 on full training set
## [1] "myfit_mdl: train complete: 7.401000 secs"

## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = "Interact.High.cor.Y", : model's bestTune found at an
## extreme of tuneGrid for parameter: alpha

##             Length Class      Mode     
## a0            69   -none-     numeric  
## beta        3588   dgCMatrix  S4       
## df            69   -none-     numeric  
## dim            2   -none-     numeric  
## lambda        69   -none-     numeric  
## dev.ratio     69   -none-     numeric  
## nulldev        1   -none-     numeric  
## npasses        1   -none-     numeric  
## jerr           1   -none-     numeric  
## offset         1   -none-     logical  
## classnames     2   -none-     character
## call           5   -none-     call     
## nobs           1   -none-     numeric  
## lambdaOpt      1   -none-     numeric  
## xNames        52   -none-     character
## problemType    1   -none-     character
## tuneValue      2   data.frame list     
## obsLevels      2   -none-     character
## [1] "min lambda > lambdaOpt:"
##                        (Intercept)                       Gender.fctrM 
##                        0.214462392                       -0.145896015 
##                     Q109244.fctrNo                    Q109244.fctrYes 
##                       -0.216275985                        0.903504361 
##      Q109244.fctrNA:Q100689.fctrNo     Q109244.fctrYes:Q100689.fctrNo 
##                        0.208349081                       -0.004870424 
##     Q109244.fctrNA:Q100689.fctrYes     Q109244.fctrNo:Q100689.fctrYes 
##                        0.397412088                        0.048879968 
##    Q109244.fctrYes:Q100689.fctrYes      Q109244.fctrNA:Q106272.fctrNo 
##                        0.262128025                        0.066457216 
##      Q109244.fctrNo:Q106272.fctrNo     Q109244.fctrYes:Q106272.fctrNo 
##                        0.074008287                       -0.069508937 
##     Q109244.fctrNA:Q106272.fctrYes     Q109244.fctrNo:Q106272.fctrYes 
##                       -0.125437842                       -0.155157463 
##    Q109244.fctrYes:Q106272.fctrYes  Q109244.fctrNA:Q108855.fctrUmm... 
##                        0.019996659                       -0.325540110 
##  Q109244.fctrNo:Q108855.fctrUmm... Q109244.fctrYes:Q108855.fctrUmm... 
##                        0.065090912                        0.025966636 
##    Q109244.fctrNo:Q108855.fctrYes!     Q109244.fctrNA:Q120472.fctrArt 
##                       -0.166309419                        0.045175676 
##    Q109244.fctrYes:Q120472.fctrArt Q109244.fctrNA:Q120472.fctrScience 
##                        0.050613747                       -0.081869305 
## Q109244.fctrNo:Q120472.fctrScience      Q109244.fctrNA:Q122771.fctrPc 
##                       -0.045206278                        0.026381445 
##      Q109244.fctrNo:Q122771.fctrPc      Q109244.fctrNA:Q122771.fctrPt 
##                       -0.079571797                       -0.056075353 
##      Q109244.fctrNo:Q122771.fctrPt     Q109244.fctrYes:Q122771.fctrPt 
##                       -0.312370610                       -0.184990100 
##      Q109244.fctrNA:Q123621.fctrNo     Q109244.fctrYes:Q123621.fctrNo 
##                       -0.055648138                        0.250541256 
##     Q109244.fctrNo:Q123621.fctrYes    Q109244.fctrYes:Q123621.fctrYes 
##                       -0.118706388                        0.234767924 
##       Q109244.fctrNA:Q98078.fctrNo       Q109244.fctrNo:Q98078.fctrNo 
##                        0.041213383                        0.052285528 
##      Q109244.fctrNA:Q98078.fctrYes     Q109244.fctrYes:Q98078.fctrYes 
##                        0.124665272                        0.101993831 
##       Q109244.fctrNA:Q99480.fctrNo       Q109244.fctrNo:Q99480.fctrNo 
##                        0.285510277                        0.345084748 
##      Q109244.fctrYes:Q99480.fctrNo      Q109244.fctrNA:Q99480.fctrYes 
##                        0.054445838                       -0.272288871 
## [1] "max lambda < lambdaOpt:"
##                        (Intercept)                       Gender.fctrM 
##                        0.215265192                       -0.146631129 
##                     Q109244.fctrNo                    Q109244.fctrYes 
##                       -0.232130198                        0.919519640 
##      Q109244.fctrNA:Q100689.fctrNo     Q109244.fctrYes:Q100689.fctrNo 
##                        0.218265269                       -0.019284614 
##     Q109244.fctrNA:Q100689.fctrYes     Q109244.fctrNo:Q100689.fctrYes 
##                        0.407359039                        0.052982570 
##    Q109244.fctrYes:Q100689.fctrYes      Q109244.fctrNA:Q106272.fctrNo 
##                        0.257862986                        0.066427041 
##      Q109244.fctrNo:Q106272.fctrNo     Q109244.fctrYes:Q106272.fctrNo 
##                        0.075834582                       -0.093768371 
##     Q109244.fctrNA:Q106272.fctrYes     Q109244.fctrNo:Q106272.fctrYes 
##                       -0.132132143                       -0.157097270 
##    Q109244.fctrYes:Q106272.fctrYes  Q109244.fctrNA:Q108855.fctrUmm... 
##                        0.006954145                       -0.334091989 
##  Q109244.fctrNo:Q108855.fctrUmm... Q109244.fctrYes:Q108855.fctrUmm... 
##                        0.073183683                        0.029348178 
##    Q109244.fctrNo:Q108855.fctrYes!     Q109244.fctrNA:Q120472.fctrArt 
##                       -0.161489197                        0.047013857 
##    Q109244.fctrYes:Q120472.fctrArt Q109244.fctrNA:Q120472.fctrScience 
##                        0.053722253                       -0.085734575 
## Q109244.fctrNo:Q120472.fctrScience      Q109244.fctrNA:Q122771.fctrPc 
##                       -0.046328701                        0.031858306 
##      Q109244.fctrNo:Q122771.fctrPc      Q109244.fctrNA:Q122771.fctrPt 
##                       -0.086494530                       -0.058868145 
##      Q109244.fctrNo:Q122771.fctrPt     Q109244.fctrYes:Q122771.fctrPt 
##                       -0.320607007                       -0.200384753 
##      Q109244.fctrNA:Q123621.fctrNo     Q109244.fctrYes:Q123621.fctrNo 
##                       -0.062593721                        0.268705396 
##     Q109244.fctrNo:Q123621.fctrYes    Q109244.fctrYes:Q123621.fctrYes 
##                       -0.120160110                        0.251231071 
##       Q109244.fctrNA:Q98078.fctrNo       Q109244.fctrNo:Q98078.fctrNo 
##                        0.050653191                        0.069505105 
##      Q109244.fctrNA:Q98078.fctrYes      Q109244.fctrNo:Q98078.fctrYes 
##                        0.134149587                        0.017836961 
##     Q109244.fctrYes:Q98078.fctrYes       Q109244.fctrNA:Q99480.fctrNo 
##                        0.109598128                        0.281737023 
##       Q109244.fctrNo:Q99480.fctrNo      Q109244.fctrYes:Q99480.fctrNo 
##                        0.348068744                        0.053827527 
##      Q109244.fctrNA:Q99480.fctrYes     Q109244.fctrYes:Q99480.fctrYes 
##                       -0.284681230                       -0.012138329 
## [1] "myfit_mdl: train diagnostics complete: 8.140000 secs"

##          Prediction
## Reference    R    D
##         R 1929  162
##         D 1715  642
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   5.780126e-01   1.870655e-01   5.633390e-01   5.925837e-01   5.299011e-01 
## AccuracyPValue  McnemarPValue 
##   6.342747e-11  4.877355e-281

##          Prediction
## Reference   R   D
##         R 481  45
##         D 433 161
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   5.732143e-01   1.779779e-01   5.436402e-01   6.024028e-01   5.303571e-01 
## AccuracyPValue  McnemarPValue 
##   2.187684e-03   4.121616e-70 
## [1] "myfit_mdl: predict complete: 14.052000 secs"
##                                id
## 1 Interact.High.cor.Y##rcv#glmnet
##                                                                                                                                                                                                                                    feats
## 1 Q109244.fctr,Gender.fctr,Q109244.fctr:Q99480.fctr,Q109244.fctr:Q98078.fctr,Q109244.fctr:Q100689.fctr,Q109244.fctr:Q108855.fctr,Q109244.fctr:Q123621.fctr,Q109244.fctr:Q122771.fctr,Q109244.fctr:Q120472.fctr,Q109244.fctr:Q106272.fctr
##   max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1              25                      6.609                  0.36
##   max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1       0.6184781    0.5958871    0.6410692       0.3319465
##   opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1                   0.65       0.6727114        0.6058167
##   max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1              0.563339             0.5925837     0.2088694
##   max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1       0.6031353    0.5665399    0.6397306       0.3571392
##   opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1                   0.65       0.6680556        0.5732143
##   max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1             0.5436402             0.6024028     0.1779779
##   max.AccuracySD.fit max.KappaSD.fit
## 1          0.0131213      0.02732571
## [1] "myfit_mdl: exit: 14.069000 secs"

# Low.cor.X
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df, 
                        paste0("fit.models_0_", "Low.cor.X"), major.inc = FALSE,
                                     label.minor = "glmnet")

##                              label step_major step_minor label_minor
## 5 fit.models_0_Interact.High.cor.Y          1          4      glmnet
## 6           fit.models_0_Low.cor.X          1          5      glmnet
##       bgn     end elapsed
## 5 292.746 306.846  14.101
## 6 306.847      NA      NA

indepVar <- mygetIndepVar(glb_feats_df)
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
            id.prefix = "Low.cor.X", 
            type = glb_model_type, 
            tune.df = glbMdlTuneParams,        
            trainControl.method = "repeatedcv",
            trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
            trainControl.classProbs = glb_is_classification,
            trainControl.summaryFunction = glbMdlMetricSummaryFn,
            trainControl.allowParallel = glbMdlAllowParallel,
            train.metric = glbMdlMetricSummary, 
            train.maximize = glbMdlMetricMaximize,    
            train.method = "glmnet")),
        indepVar = indepVar, rsp_var = glb_rsp_var, 
        fit_df = glbObsFit, OOB_df = glbObsOOB)

## [1] "myfit_mdl: enter: 0.000000 secs"
## [1] "fitting model: Low.cor.X##rcv#glmnet"
## [1] "    indepVar: Q109244.fctr,Hhold.fctr,Edn.fctr,Q101163.fctr,Q100689.fctr,Q98078.fctr,Q99716.fctr,Q120379.fctr,Q121699.fctr,Q105840.fctr,Q113583.fctr,Q115195.fctr,Q102089.fctr,Q98059.fctr,Q114386.fctr,Q100680.fctr,Q108342.fctr,Q111848.fctr,YOB.Age.fctr,Q118892.fctr,Q102687.fctr,Q115390.fctr,Q119851.fctr,Q114517.fctr,Q120012.fctr,Q109367.fctr,Q114961.fctr,Q121700.fctr,Q124122.fctr,Q111220.fctr,Q113992.fctr,Q121011.fctr,Q106042.fctr,Q116448.fctr,Q116601.fctr,Q104996.fctr,Q102906.fctr,Q113584.fctr,Q108950.fctr,Q102674.fctr,Q103293.fctr,Q112478.fctr,Q114748.fctr,Q107491.fctr,Q100562.fctr,Q108617.fctr,Q100010.fctr,Q115602.fctr,Q116953.fctr,Q115610.fctr,Q106997.fctr,Q120978.fctr,Q112512.fctr,Q108343.fctr,Q96024.fctr,Q106389.fctr,.rnorm,Q108754.fctr,Q98578.fctr,Q101162.fctr,Q115777.fctr,Q99581.fctr,Q124742.fctr,Q116797.fctr,Q112270.fctr,Q118237.fctr,Q119650.fctr,Q111580.fctr,Q123464.fctr,Q117193.fctr,Q99982.fctr,Q108856.fctr,Q118233.fctr,Q102289.fctr,Q116197.fctr,Income.fctr,Q118232.fctr,Q120194.fctr,Q114152.fctr,Q122770.fctr,Q117186.fctr,Q105655.fctr,Q106993.fctr,Q119334.fctr,Q122120.fctr,Q116441.fctr,Q118117.fctr,Q123621.fctr,Q122769.fctr,Q120650.fctr,Q98869.fctr,Q107869.fctr,Q120014.fctr,Q115899.fctr,Q106388.fctr,Q99480.fctr,Q122771.fctr,Q108855.fctr,Q110740.fctr,Q106272.fctr,Q101596.fctr,Q116881.fctr,Q120472.fctr,Q98197.fctr,Q113181.fctr,Q115611.fctr,Gender.fctr,Hhold.fctr:.clusterid.fctr"
## [1] "myfit_mdl: setup complete: 0.712000 secs"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.325, lambda = 0.0534 on full training set
## [1] "myfit_mdl: train complete: 25.171000 secs"

## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = "Low.cor.X", : model's bestTune found at an extreme of
## tuneGrid for parameter: lambda

##             Length Class      Mode     
## a0             78  -none-     numeric  
## beta        19734  dgCMatrix  S4       
## df             78  -none-     numeric  
## dim             2  -none-     numeric  
## lambda         78  -none-     numeric  
## dev.ratio      78  -none-     numeric  
## nulldev         1  -none-     numeric  
## npasses         1  -none-     numeric  
## jerr            1  -none-     numeric  
## offset          1  -none-     logical  
## classnames      2  -none-     character
## call            5  -none-     call     
## nobs            1  -none-     numeric  
## lambdaOpt       1  -none-     numeric  
## xNames        253  -none-     character
## problemType     1  -none-     character
## tuneValue       2  data.frame list     
## obsLevels       2  -none-     character
## [1] "min lambda > lambdaOpt:"
##       (Intercept)      Gender.fctrM     Hhold.fctrMKy     Hhold.fctrPKn 
##      0.1759341202     -0.0577396529     -0.0655451865      0.3282606962 
##   Q101163.fctrDad   Q101163.fctrMom    Q109244.fctrNo   Q109244.fctrYes 
##     -0.0462347606      0.0361283260     -0.3170070971      0.7976156342 
##    Q110740.fctrPC    Q113181.fctrNo   Q113181.fctrYes    Q115611.fctrNo 
##     -0.0120813108      0.0348921948     -0.0798654082      0.0770473552 
##   Q115611.fctrYes Q116881.fctrHappy Q116881.fctrRight    Q118232.fctrId 
##     -0.3105852699      0.0087382029     -0.0814535814      0.0180660706 
##    Q119851.fctrNo    Q120379.fctrNo   Q120379.fctrYes     Q98197.fctrNo 
##     -0.0297897358     -0.0006993182      0.0272478036      0.1639383807 
##    Q98197.fctrYes     Q98869.fctrNo     Q99480.fctrNo 
##     -0.0341915807      0.1609634790      0.0576623212 
## [1] "max lambda < lambdaOpt:"
##       (Intercept)      Gender.fctrM     Hhold.fctrMKy     Hhold.fctrPKn 
##       0.180590790      -0.063356510      -0.071963008       0.358397790 
##     Income.fctr.Q   Q101163.fctrDad   Q101163.fctrMom    Q109244.fctrNo 
##      -0.003003778      -0.055383160       0.041352778      -0.326547933 
##   Q109244.fctrYes    Q110740.fctrPC    Q113181.fctrNo   Q113181.fctrYes 
##       0.811987955      -0.023695743       0.041178168      -0.082431818 
##    Q115611.fctrNo   Q115611.fctrYes    Q115899.fctrCs Q116881.fctrHappy 
##       0.080608650      -0.318215302       0.001114558       0.019171363 
## Q116881.fctrRight    Q118232.fctrId    Q119851.fctrNo    Q120379.fctrNo 
##      -0.090653797       0.032794176      -0.043521175      -0.004314002 
##   Q120379.fctrYes    Q121699.fctrNo    Q122771.fctrPt     Q98197.fctrNo 
##       0.038379682      -0.010439127      -0.000907088       0.171596860 
##    Q98197.fctrYes     Q98869.fctrNo     Q99480.fctrNo 
##      -0.034226771       0.174722108       0.068918234 
## [1] "myfit_mdl: train diagnostics complete: 25.883000 secs"

##          Prediction
## Reference    R    D
##         R 1911  180
##         D 1655  702
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   5.874550e-01   2.036470e-01   5.728218e-01   6.019734e-01   5.299011e-01 
## AccuracyPValue  McnemarPValue 
##   6.718265e-15  1.812597e-259

##          Prediction
## Reference   R   D
##         R 494  32
##         D 467 127
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   5.544643e-01   1.460545e-01   5.247985e-01   5.838432e-01   5.303571e-01 
## AccuracyPValue  McnemarPValue 
##   5.617624e-02   4.430516e-84 
## [1] "myfit_mdl: predict complete: 35.996000 secs"
##                      id
## 1 Low.cor.X##rcv#glmnet
##                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               feats
## 1 Q109244.fctr,Hhold.fctr,Edn.fctr,Q101163.fctr,Q100689.fctr,Q98078.fctr,Q99716.fctr,Q120379.fctr,Q121699.fctr,Q105840.fctr,Q113583.fctr,Q115195.fctr,Q102089.fctr,Q98059.fctr,Q114386.fctr,Q100680.fctr,Q108342.fctr,Q111848.fctr,YOB.Age.fctr,Q118892.fctr,Q102687.fctr,Q115390.fctr,Q119851.fctr,Q114517.fctr,Q120012.fctr,Q109367.fctr,Q114961.fctr,Q121700.fctr,Q124122.fctr,Q111220.fctr,Q113992.fctr,Q121011.fctr,Q106042.fctr,Q116448.fctr,Q116601.fctr,Q104996.fctr,Q102906.fctr,Q113584.fctr,Q108950.fctr,Q102674.fctr,Q103293.fctr,Q112478.fctr,Q114748.fctr,Q107491.fctr,Q100562.fctr,Q108617.fctr,Q100010.fctr,Q115602.fctr,Q116953.fctr,Q115610.fctr,Q106997.fctr,Q120978.fctr,Q112512.fctr,Q108343.fctr,Q96024.fctr,Q106389.fctr,.rnorm,Q108754.fctr,Q98578.fctr,Q101162.fctr,Q115777.fctr,Q99581.fctr,Q124742.fctr,Q116797.fctr,Q112270.fctr,Q118237.fctr,Q119650.fctr,Q111580.fctr,Q123464.fctr,Q117193.fctr,Q99982.fctr,Q108856.fctr,Q118233.fctr,Q102289.fctr,Q116197.fctr,Income.fctr,Q118232.fctr,Q120194.fctr,Q114152.fctr,Q122770.fctr,Q117186.fctr,Q105655.fctr,Q106993.fctr,Q119334.fctr,Q122120.fctr,Q116441.fctr,Q118117.fctr,Q123621.fctr,Q122769.fctr,Q120650.fctr,Q98869.fctr,Q107869.fctr,Q120014.fctr,Q115899.fctr,Q106388.fctr,Q99480.fctr,Q122771.fctr,Q108855.fctr,Q110740.fctr,Q106272.fctr,Q101596.fctr,Q116881.fctr,Q120472.fctr,Q98197.fctr,Q113181.fctr,Q115611.fctr,Gender.fctr,Hhold.fctr:.clusterid.fctr
##   max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1              25                     24.339                  2.12
##   max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1       0.6273089    0.5155428    0.7390751       0.3048695
##   opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1                    0.6       0.6756231        0.6247001
##   max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1             0.5728218             0.6019734     0.2399024
##   max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1       0.6237374          0.5    0.7474747       0.3184875
##   opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1                    0.7        0.664425        0.5544643
##   max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1             0.5247985             0.5838432     0.1460545
##   max.AccuracySD.fit max.KappaSD.fit
## 1        0.004682542      0.01138792
## [1] "myfit_mdl: exit: 36.011000 secs"

fit.models_0_chunk_df <- 
    myadd_chunk(fit.models_0_chunk_df, "fit.models_0_end", major.inc = FALSE,
                label.minor = "teardown")

##                    label step_major step_minor label_minor     bgn     end
## 6 fit.models_0_Low.cor.X          1          5      glmnet 306.847 342.908
## 7       fit.models_0_end          1          6    teardown 342.909      NA
##   elapsed
## 6  36.061
## 7      NA

rm(ret_lst)

glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.models", major.inc = FALSE)

##        label step_major step_minor label_minor     bgn     end elapsed
## 4 fit.models          4          0           0 259.959 342.922  82.963
## 5 fit.models          4          1           1 342.922      NA      NA

fit.models_1_chunk_df <- myadd_chunk(NULL, "fit.models_1_bgn", label.minor = "setup")

##              label step_major step_minor label_minor    bgn end elapsed
## 1 fit.models_1_bgn          1          0       setup 347.28  NA      NA

# refactor code for outliers / ensure all model runs exclude outliers in this chunk ???

#stop(here"); glb2Sav(); all.equal(glb_models_df, sav_models_df)
topindep_var <- NULL; interact_vars <- NULL;
for (mdl_id_pfx in names(glbMdlFamilies)) {
    fit.models_1_chunk_df <- 
        myadd_chunk(fit.models_1_chunk_df, paste0("fit.models_1_", mdl_id_pfx),
                    major.inc = FALSE, label.minor = "setup")

    indepVar <- NULL;

    if (grepl("\\.Interact", mdl_id_pfx)) {
        if (is.null(topindep_var) && is.null(interact_vars)) {
        #   select best glmnet model upto now
            dsp_models_df <- orderBy(model_sel_frmla <- get_model_sel_frmla(),
                                     glb_models_df)
            dsp_models_df <- subset(dsp_models_df, 
                                    grepl(".glmnet", id, fixed = TRUE))
            bst_mdl_id <- dsp_models_df$id[1]
            mdl_id_pfx <- 
                paste(c(head(unlist(strsplit(bst_mdl_id, "[.]")), -1), "Interact"),
                      collapse=".")
        #   select important features
            if (is.null(bst_featsimp_df <- 
                        myget_feats_importance(glb_models_lst[[bst_mdl_id]]))) {
                warning("Base model for RFE.Interact: ", bst_mdl_id, 
                        " has no important features")
                next
            }    
            
            topindep_ix <- 1
            while (is.null(topindep_var) && (topindep_ix <= nrow(bst_featsimp_df))) {
                topindep_var <- row.names(bst_featsimp_df)[topindep_ix]
                if (grepl(".fctr", topindep_var, fixed=TRUE))
                    topindep_var <- 
                        paste0(unlist(strsplit(topindep_var, ".fctr"))[1], ".fctr")
                if (topindep_var %in% names(glbFeatsInteractionOnly)) {
                    topindep_var <- NULL; topindep_ix <- topindep_ix + 1
                } else break
            }
            
        #   select features with importance > max(10, importance of .rnorm) & is not highest
        #       combine factor dummy features to just the factor feature
            if (length(pos_rnorm <- 
                       grep(".rnorm", row.names(bst_featsimp_df), fixed=TRUE)) > 0)
                imp_rnorm <- bst_featsimp_df[pos_rnorm, 1] else
                imp_rnorm <- NA    
            imp_cutoff <- max(10, imp_rnorm, na.rm=TRUE)
            interact_vars <- 
                tail(row.names(subset(bst_featsimp_df, 
                                      imp > imp_cutoff)), -1)
            if (length(interact_vars) > 0) {
                interact_vars <-
                    myadjustInteractionFeats(glb_feats_df, myextract_actual_feats(interact_vars))
                interact_vars <- 
                    interact_vars[!grepl(topindep_var, interact_vars, fixed=TRUE)]
            }
            ### bid0_sp only
#             interact_vars <- c(
#     "biddable", "D.ratio.sum.TfIdf.wrds.n", "D.TfIdf.sum.stem.stop.Ratio", "D.sum.TfIdf",
#     "D.TfIdf.sum.post.stop", "D.TfIdf.sum.post.stem", "D.ratio.wrds.stop.n.wrds.n", "D.chrs.uppr.n.log",
#     "D.chrs.n.log", "color.fctr"
#     # , "condition.fctr", "prdl.my.descr.fctr"
#                                 )
#            interact_vars <- setdiff(interact_vars, c("startprice.dgt2.is9", "color.fctr"))
            ###
            indepVar <- myextract_actual_feats(row.names(bst_featsimp_df))
            indepVar <- setdiff(indepVar, topindep_var)
            if (length(interact_vars) > 0) {
                indepVar <- 
                    setdiff(indepVar, myextract_actual_feats(interact_vars))
                indepVar <- c(indepVar, 
                    paste(topindep_var, setdiff(interact_vars, topindep_var), 
                          sep = "*"))
            } else indepVar <- union(indepVar, topindep_var)
        }
    }
    
    if (is.null(indepVar))
        indepVar <- glb_mdl_feats_lst[[mdl_id_pfx]]

    if (is.null(indepVar) && grepl("RFE\\.", mdl_id_pfx))
        indepVar <- myextract_actual_feats(predictors(rfe_fit_results))
    
    if (is.null(indepVar))
        indepVar <- mygetIndepVar(glb_feats_df)
    
    if ((length(indepVar) == 1) && (grepl("^%<d-%", indepVar))) {    
        indepVar <- 
            eval(parse(text = str_trim(unlist(strsplit(indepVar, "%<d-%"))[2])))
    }    

    indepVar <- myadjustInteractionFeats(glb_feats_df, indepVar)
    
    if (grepl("\\.Interact", mdl_id_pfx)) { 
        # if (method != tail(unlist(strsplit(bst_mdl_id, "[.]")), 1)) next
        if (is.null(glbMdlFamilies[[mdl_id_pfx]])) {
            if (!is.null(glbMdlFamilies[["Best.Interact"]]))
                glbMdlFamilies[[mdl_id_pfx]] <-
                    glbMdlFamilies[["Best.Interact"]]
        }
    }
    
    if (!is.null(glbObsFitOutliers[[mdl_id_pfx]])) {
        fitobs_df <- glbObsFit[!(glbObsFit[, glbFeatsId] %in%
                                         glbObsFitOutliers[[mdl_id_pfx]]), ]
        print(sprintf("Outliers removed: %d", nrow(glbObsFit) - nrow(fitobs_df)))
        print(setdiff(glbObsFit[, glbFeatsId], fitobs_df[, glbFeatsId]))
    } else fitobs_df <- glbObsFit

    if (is.null(glbMdlFamilies[[mdl_id_pfx]]))
        mdl_methods <- glbMdlMethods else
        mdl_methods <- glbMdlFamilies[[mdl_id_pfx]]    

    for (method in mdl_methods) {
        if (method %in% c("rpart", "rf")) {
            # rpart:    fubar's the tree
            # rf:       skip the scenario w/ .rnorm for speed
            indepVar <- setdiff(indepVar, c(".rnorm"))
            #mdl_id <- paste0(mdl_id_pfx, ".no.rnorm")
        } 

        fit.models_1_chunk_df <- myadd_chunk(fit.models_1_chunk_df, 
                            paste0("fit.models_1_", mdl_id_pfx), major.inc = FALSE,
                                    label.minor = method)

        ret_lst <- 
            myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
            id.prefix = mdl_id_pfx, 
            type = glb_model_type, 
            tune.df = glbMdlTuneParams,
            trainControl.method = "repeatedcv", # or "none" if nominalWorkflow is crashing
            trainControl.number = glb_rcv_n_folds,
            trainControl.repeats = glb_rcv_n_repeats,
            trainControl.classProbs = glb_is_classification,
            trainControl.summaryFunction = glbMdlMetricSummaryFn,
            trainControl.allowParallel = glbMdlAllowParallel,            
            train.metric = glbMdlMetricSummary, 
            train.maximize = glbMdlMetricMaximize,    
            train.method = method)),
            indepVar = indepVar, rsp_var = glb_rsp_var, 
            fit_df = fitobs_df, OOB_df = glbObsOOB)
        
#         ntv_mdl <- glmnet(x = as.matrix(
#                               fitobs_df[, indepVar]), 
#                           y = as.factor(as.character(
#                               fitobs_df[, glb_rsp_var])),
#                           family = "multinomial")
#         bgn = 1; end = 100;
#         ntv_mdl <- glmnet(x = as.matrix(
#                               subset(fitobs_df, pop.fctr != "crypto")[bgn:end, indepVar]), 
#                           y = as.factor(as.character(
#                               subset(fitobs_df, pop.fctr != "crypto")[bgn:end, glb_rsp_var])),
#                           family = "multinomial")
    }
}

##                label step_major step_minor label_minor     bgn    end
## 1   fit.models_1_bgn          1          0       setup 347.280 347.29
## 2 fit.models_1_All.X          1          1       setup 347.291     NA
##   elapsed
## 1    0.01
## 2      NA
##                label step_major step_minor label_minor     bgn     end
## 2 fit.models_1_All.X          1          1       setup 347.291 347.298
## 3 fit.models_1_All.X          1          2      glmnet 347.298      NA
##   elapsed
## 2   0.007
## 3      NA
## [1] "myfit_mdl: enter: 0.000000 secs"
## [1] "fitting model: All.X##rcv#glmnet"
## [1] "    indepVar: Q109244.fctr,Hhold.fctr,Edn.fctr,Q101163.fctr,Q100689.fctr,Q98078.fctr,Q99716.fctr,Q120379.fctr,Q121699.fctr,Q105840.fctr,Q113583.fctr,Q115195.fctr,Q102089.fctr,Q98059.fctr,Q114386.fctr,Q100680.fctr,Q108342.fctr,Q111848.fctr,YOB.Age.fctr,Q118892.fctr,Q102687.fctr,Q115390.fctr,Q119851.fctr,Q114517.fctr,Q120012.fctr,Q109367.fctr,Q114961.fctr,Q121700.fctr,Q124122.fctr,Q111220.fctr,Q113992.fctr,Q121011.fctr,Q106042.fctr,Q116448.fctr,Q116601.fctr,Q104996.fctr,Q102906.fctr,Q113584.fctr,Q108950.fctr,Q102674.fctr,Q103293.fctr,Q112478.fctr,Q114748.fctr,Q107491.fctr,Q100562.fctr,Q108617.fctr,Q100010.fctr,Q115602.fctr,Q116953.fctr,Q115610.fctr,Q106997.fctr,Q120978.fctr,Q112512.fctr,Q108343.fctr,Q96024.fctr,Q106389.fctr,.rnorm,Q108754.fctr,Q98578.fctr,Q101162.fctr,Q115777.fctr,Q99581.fctr,Q124742.fctr,Q116797.fctr,Q112270.fctr,Q118237.fctr,Q119650.fctr,Q111580.fctr,Q123464.fctr,Q117193.fctr,Q99982.fctr,Q108856.fctr,Q118233.fctr,Q102289.fctr,Q116197.fctr,Income.fctr,Q118232.fctr,Q120194.fctr,Q114152.fctr,Q122770.fctr,Q117186.fctr,Q105655.fctr,Q106993.fctr,Q119334.fctr,Q122120.fctr,Q116441.fctr,Q118117.fctr,Q123621.fctr,Q122769.fctr,Q120650.fctr,Q98869.fctr,Q107869.fctr,Q120014.fctr,Q115899.fctr,Q106388.fctr,Q99480.fctr,Q122771.fctr,Q108855.fctr,Q110740.fctr,Q106272.fctr,Q101596.fctr,Q116881.fctr,Q120472.fctr,Q98197.fctr,Q113181.fctr,Q115611.fctr,Gender.fctr,Hhold.fctr:.clusterid.fctr"
## [1] "myfit_mdl: setup complete: 0.695000 secs"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.325, lambda = 0.0534 on full training set
## [1] "myfit_mdl: train complete: 24.913000 secs"

## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = mdl_id_pfx, : model's bestTune found at an extreme of
## tuneGrid for parameter: lambda

##             Length Class      Mode     
## a0             78  -none-     numeric  
## beta        19734  dgCMatrix  S4       
## df             78  -none-     numeric  
## dim             2  -none-     numeric  
## lambda         78  -none-     numeric  
## dev.ratio      78  -none-     numeric  
## nulldev         1  -none-     numeric  
## npasses         1  -none-     numeric  
## jerr            1  -none-     numeric  
## offset          1  -none-     logical  
## classnames      2  -none-     character
## call            5  -none-     call     
## nobs            1  -none-     numeric  
## lambdaOpt       1  -none-     numeric  
## xNames        253  -none-     character
## problemType     1  -none-     character
## tuneValue       2  data.frame list     
## obsLevels       2  -none-     character
## [1] "min lambda > lambdaOpt:"
##       (Intercept)      Gender.fctrM     Hhold.fctrMKy     Hhold.fctrPKn 
##      0.1759341202     -0.0577396529     -0.0655451865      0.3282606962 
##   Q101163.fctrDad   Q101163.fctrMom    Q109244.fctrNo   Q109244.fctrYes 
##     -0.0462347606      0.0361283260     -0.3170070971      0.7976156342 
##    Q110740.fctrPC    Q113181.fctrNo   Q113181.fctrYes    Q115611.fctrNo 
##     -0.0120813108      0.0348921948     -0.0798654082      0.0770473552 
##   Q115611.fctrYes Q116881.fctrHappy Q116881.fctrRight    Q118232.fctrId 
##     -0.3105852699      0.0087382029     -0.0814535814      0.0180660706 
##    Q119851.fctrNo    Q120379.fctrNo   Q120379.fctrYes     Q98197.fctrNo 
##     -0.0297897358     -0.0006993182      0.0272478036      0.1639383807 
##    Q98197.fctrYes     Q98869.fctrNo     Q99480.fctrNo 
##     -0.0341915807      0.1609634790      0.0576623212 
## [1] "max lambda < lambdaOpt:"
##       (Intercept)      Gender.fctrM     Hhold.fctrMKy     Hhold.fctrPKn 
##       0.180590790      -0.063356510      -0.071963008       0.358397790 
##     Income.fctr.Q   Q101163.fctrDad   Q101163.fctrMom    Q109244.fctrNo 
##      -0.003003778      -0.055383160       0.041352778      -0.326547933 
##   Q109244.fctrYes    Q110740.fctrPC    Q113181.fctrNo   Q113181.fctrYes 
##       0.811987955      -0.023695743       0.041178168      -0.082431818 
##    Q115611.fctrNo   Q115611.fctrYes    Q115899.fctrCs Q116881.fctrHappy 
##       0.080608650      -0.318215302       0.001114558       0.019171363 
## Q116881.fctrRight    Q118232.fctrId    Q119851.fctrNo    Q120379.fctrNo 
##      -0.090653797       0.032794176      -0.043521175      -0.004314002 
##   Q120379.fctrYes    Q121699.fctrNo    Q122771.fctrPt     Q98197.fctrNo 
##       0.038379682      -0.010439127      -0.000907088       0.171596860 
##    Q98197.fctrYes     Q98869.fctrNo     Q99480.fctrNo 
##      -0.034226771       0.174722108       0.068918234 
## [1] "myfit_mdl: train diagnostics complete: 25.700000 secs"

##          Prediction
## Reference    R    D
##         R 1911  180
##         D 1655  702
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   5.874550e-01   2.036470e-01   5.728218e-01   6.019734e-01   5.299011e-01 
## AccuracyPValue  McnemarPValue 
##   6.718265e-15  1.812597e-259

##          Prediction
## Reference   R   D
##         R 494  32
##         D 467 127
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   5.544643e-01   1.460545e-01   5.247985e-01   5.838432e-01   5.303571e-01 
## AccuracyPValue  McnemarPValue 
##   5.617624e-02   4.430516e-84 
## [1] "myfit_mdl: predict complete: 36.379000 secs"
##                  id
## 1 All.X##rcv#glmnet
##                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               feats
## 1 Q109244.fctr,Hhold.fctr,Edn.fctr,Q101163.fctr,Q100689.fctr,Q98078.fctr,Q99716.fctr,Q120379.fctr,Q121699.fctr,Q105840.fctr,Q113583.fctr,Q115195.fctr,Q102089.fctr,Q98059.fctr,Q114386.fctr,Q100680.fctr,Q108342.fctr,Q111848.fctr,YOB.Age.fctr,Q118892.fctr,Q102687.fctr,Q115390.fctr,Q119851.fctr,Q114517.fctr,Q120012.fctr,Q109367.fctr,Q114961.fctr,Q121700.fctr,Q124122.fctr,Q111220.fctr,Q113992.fctr,Q121011.fctr,Q106042.fctr,Q116448.fctr,Q116601.fctr,Q104996.fctr,Q102906.fctr,Q113584.fctr,Q108950.fctr,Q102674.fctr,Q103293.fctr,Q112478.fctr,Q114748.fctr,Q107491.fctr,Q100562.fctr,Q108617.fctr,Q100010.fctr,Q115602.fctr,Q116953.fctr,Q115610.fctr,Q106997.fctr,Q120978.fctr,Q112512.fctr,Q108343.fctr,Q96024.fctr,Q106389.fctr,.rnorm,Q108754.fctr,Q98578.fctr,Q101162.fctr,Q115777.fctr,Q99581.fctr,Q124742.fctr,Q116797.fctr,Q112270.fctr,Q118237.fctr,Q119650.fctr,Q111580.fctr,Q123464.fctr,Q117193.fctr,Q99982.fctr,Q108856.fctr,Q118233.fctr,Q102289.fctr,Q116197.fctr,Income.fctr,Q118232.fctr,Q120194.fctr,Q114152.fctr,Q122770.fctr,Q117186.fctr,Q105655.fctr,Q106993.fctr,Q119334.fctr,Q122120.fctr,Q116441.fctr,Q118117.fctr,Q123621.fctr,Q122769.fctr,Q120650.fctr,Q98869.fctr,Q107869.fctr,Q120014.fctr,Q115899.fctr,Q106388.fctr,Q99480.fctr,Q122771.fctr,Q108855.fctr,Q110740.fctr,Q106272.fctr,Q101596.fctr,Q116881.fctr,Q120472.fctr,Q98197.fctr,Q113181.fctr,Q115611.fctr,Gender.fctr,Hhold.fctr:.clusterid.fctr
##   max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1              25                     24.094                 2.105
##   max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1       0.6273089    0.5155428    0.7390751       0.3048695
##   opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1                    0.6       0.6756231        0.6247001
##   max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1             0.5728218             0.6019734     0.2399024
##   max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1       0.6237374          0.5    0.7474747       0.3184875
##   opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1                    0.7        0.664425        0.5544643
##   max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1             0.5247985             0.5838432     0.1460545
##   max.AccuracySD.fit max.KappaSD.fit
## 1        0.004682542      0.01138792
## [1] "myfit_mdl: exit: 36.394000 secs"
##                label step_major step_minor label_minor     bgn     end
## 3 fit.models_1_All.X          1          2      glmnet 347.298 383.698
## 4 fit.models_1_All.X          1          3         glm 383.698      NA
##   elapsed
## 3    36.4
## 4      NA
## [1] "myfit_mdl: enter: 0.000000 secs"
## [1] "fitting model: All.X##rcv#glm"
## [1] "    indepVar: Q109244.fctr,Hhold.fctr,Edn.fctr,Q101163.fctr,Q100689.fctr,Q98078.fctr,Q99716.fctr,Q120379.fctr,Q121699.fctr,Q105840.fctr,Q113583.fctr,Q115195.fctr,Q102089.fctr,Q98059.fctr,Q114386.fctr,Q100680.fctr,Q108342.fctr,Q111848.fctr,YOB.Age.fctr,Q118892.fctr,Q102687.fctr,Q115390.fctr,Q119851.fctr,Q114517.fctr,Q120012.fctr,Q109367.fctr,Q114961.fctr,Q121700.fctr,Q124122.fctr,Q111220.fctr,Q113992.fctr,Q121011.fctr,Q106042.fctr,Q116448.fctr,Q116601.fctr,Q104996.fctr,Q102906.fctr,Q113584.fctr,Q108950.fctr,Q102674.fctr,Q103293.fctr,Q112478.fctr,Q114748.fctr,Q107491.fctr,Q100562.fctr,Q108617.fctr,Q100010.fctr,Q115602.fctr,Q116953.fctr,Q115610.fctr,Q106997.fctr,Q120978.fctr,Q112512.fctr,Q108343.fctr,Q96024.fctr,Q106389.fctr,.rnorm,Q108754.fctr,Q98578.fctr,Q101162.fctr,Q115777.fctr,Q99581.fctr,Q124742.fctr,Q116797.fctr,Q112270.fctr,Q118237.fctr,Q119650.fctr,Q111580.fctr,Q123464.fctr,Q117193.fctr,Q99982.fctr,Q108856.fctr,Q118233.fctr,Q102289.fctr,Q116197.fctr,Income.fctr,Q118232.fctr,Q120194.fctr,Q114152.fctr,Q122770.fctr,Q117186.fctr,Q105655.fctr,Q106993.fctr,Q119334.fctr,Q122120.fctr,Q116441.fctr,Q118117.fctr,Q123621.fctr,Q122769.fctr,Q120650.fctr,Q98869.fctr,Q107869.fctr,Q120014.fctr,Q115899.fctr,Q106388.fctr,Q99480.fctr,Q122771.fctr,Q108855.fctr,Q110740.fctr,Q106272.fctr,Q101596.fctr,Q116881.fctr,Q120472.fctr,Q98197.fctr,Q113181.fctr,Q115611.fctr,Gender.fctr,Hhold.fctr:.clusterid.fctr"
## [1] "myfit_mdl: setup complete: 0.705000 secs"
## + Fold1.Rep1: parameter=none

## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading

## - Fold1.Rep1: parameter=none 
## + Fold2.Rep1: parameter=none

## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading

## - Fold2.Rep1: parameter=none 
## + Fold3.Rep1: parameter=none

## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading

## - Fold3.Rep1: parameter=none 
## + Fold1.Rep2: parameter=none

## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading

## - Fold1.Rep2: parameter=none 
## + Fold2.Rep2: parameter=none

## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading

## - Fold2.Rep2: parameter=none 
## + Fold3.Rep2: parameter=none

## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading

## - Fold3.Rep2: parameter=none 
## + Fold1.Rep3: parameter=none

## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading

## - Fold1.Rep3: parameter=none 
## + Fold2.Rep3: parameter=none

## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading

## - Fold2.Rep3: parameter=none 
## + Fold3.Rep3: parameter=none

## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading

## - Fold3.Rep3: parameter=none 
## Aggregating results
## Fitting final model on full training set
## [1] "myfit_mdl: train complete: 19.686000 secs"

## 
## Call:
## NULL
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.6730  -1.0347   0.4125   1.0329   2.3125  
## 
## Coefficients: (5 not defined because of singularities)
##                                    Estimate Std. Error z value Pr(>|z|)
## (Intercept)                       3.528e-01  2.766e-01   1.276 0.202129
## .rnorm                           -1.780e-02  3.360e-02  -0.530 0.596221
## Edn.fctr.L                       -7.715e-02  1.556e-01  -0.496 0.620045
## Edn.fctr.Q                       -1.332e-02  1.463e-01  -0.091 0.927438
## Edn.fctr.C                       -1.876e-02  1.279e-01  -0.147 0.883342
## `Edn.fctr^4`                     -3.417e-01  1.260e-01  -2.711 0.006714
## `Edn.fctr^5`                     -6.605e-02  1.165e-01  -0.567 0.570605
## `Edn.fctr^6`                      1.382e-01  1.055e-01   1.309 0.190394
## `Edn.fctr^7`                      1.790e-01  1.170e-01   1.530 0.126137
## Gender.fctrF                     -3.729e-01  2.407e-01  -1.549 0.121395
## Gender.fctrM                     -4.460e-01  2.366e-01  -1.885 0.059460
## Hhold.fctrMKn                     4.696e-02  2.247e-01   0.209 0.834463
## Hhold.fctrMKy                    -1.281e-01  1.973e-01  -0.649 0.516287
## Hhold.fctrPKn                     7.749e-01  4.048e-01   1.914 0.055579
## Hhold.fctrPKy                     6.054e-01  5.217e-01   1.160 0.245847
## Hhold.fctrSKn                     3.517e-01  1.832e-01   1.919 0.054928
## Hhold.fctrSKy                     4.490e-01  3.233e-01   1.389 0.164822
## Income.fctr.L                    -1.004e-01  1.081e-01  -0.928 0.353207
## Income.fctr.Q                    -1.752e-01  9.927e-02  -1.765 0.077621
## Income.fctr.C                    -2.582e-01  9.667e-02  -2.670 0.007576
## `Income.fctr^4`                  -1.106e-01  9.399e-02  -1.177 0.239356
## `Income.fctr^5`                   2.375e-04  9.572e-02   0.002 0.998021
## `Income.fctr^6`                   9.437e-02  9.308e-02   1.014 0.310635
## Q100010.fctrNo                    2.503e-01  2.177e-01   1.150 0.250283
## Q100010.fctrYes                   1.743e-01  1.994e-01   0.874 0.382131
## Q100562.fctrNo                    6.245e-02  2.011e-01   0.310 0.756197
## Q100562.fctrYes                   7.530e-02  1.771e-01   0.425 0.670715
## Q100680.fctrNo                   -2.044e-01  1.936e-01  -1.056 0.291021
## Q100680.fctrYes                  -1.512e-01  1.873e-01  -0.807 0.419518
## Q100689.fctrNo                    3.969e-01  1.949e-01   2.036 0.041752
## Q100689.fctrYes                   5.604e-01  1.937e-01   2.893 0.003810
## Q101162.fctrOptimist              6.070e-02  1.802e-01   0.337 0.736253
## Q101162.fctrPessimist             4.295e-02  1.864e-01   0.230 0.817779
## Q101163.fctrDad                  -2.486e-01  1.597e-01  -1.556 0.119632
## Q101163.fctrMom                   3.838e-02  1.641e-01   0.234 0.815032
## Q101596.fctrNo                   -4.716e-01  1.637e-01  -2.881 0.003970
## Q101596.fctrYes                  -4.342e-01  1.729e-01  -2.511 0.012036
## Q102089.fctrOwn                   1.216e-01  1.644e-01   0.740 0.459312
## Q102089.fctrRent                  9.024e-02  1.740e-01   0.519 0.603927
## Q102289.fctrNo                    1.129e-01  1.686e-01   0.670 0.503127
## Q102289.fctrYes                   5.943e-02  1.795e-01   0.331 0.740515
## Q102674.fctrNo                   -4.101e-01  2.171e-01  -1.889 0.058951
## Q102674.fctrYes                  -3.770e-01  2.285e-01  -1.650 0.098879
## Q102687.fctrNo                    3.947e-01  2.316e-01   1.704 0.088403
## Q102687.fctrYes                   4.715e-01  2.298e-01   2.052 0.040203
## Q102906.fctrNo                    1.401e-02  1.709e-01   0.082 0.934669
## Q102906.fctrYes                  -1.192e-02  1.755e-01  -0.068 0.945844
## Q103293.fctrNo                   -1.071e-01  1.533e-01  -0.698 0.485018
## Q103293.fctrYes                  -1.552e-05  1.550e-01   0.000 0.999920
## Q104996.fctrNo                   -1.512e-02  1.435e-01  -0.105 0.916074
## Q104996.fctrYes                   1.486e-01  1.417e-01   1.049 0.294397
## Q105655.fctrNo                   -1.111e-01  1.746e-01  -0.636 0.524827
## Q105655.fctrYes                  -1.950e-01  1.727e-01  -1.129 0.258849
## Q105840.fctrNo                    7.601e-02  1.760e-01   0.432 0.665904
## Q105840.fctrYes                   6.942e-02  1.773e-01   0.392 0.695350
## Q106042.fctrNo                   -2.509e-01  1.746e-01  -1.436 0.150869
## Q106042.fctrYes                  -1.999e-01  1.751e-01  -1.142 0.253537
## Q106272.fctrNo                    1.535e-01  1.957e-01   0.784 0.432964
## Q106272.fctrYes                   2.756e-02  1.823e-01   0.151 0.879837
## Q106388.fctrNo                   -2.281e-02  2.142e-01  -0.107 0.915164
## Q106388.fctrYes                  -4.215e-03  2.264e-01  -0.019 0.985148
## Q106389.fctrNo                   -2.443e-01  2.126e-01  -1.149 0.250550
## Q106389.fctrYes                  -7.382e-02  2.142e-01  -0.345 0.730395
## Q106993.fctrNo                   -2.219e-01  2.157e-01  -1.029 0.303615
## Q106993.fctrYes                  -9.943e-02  1.922e-01  -0.517 0.604854
## Q106997.fctrGr                    2.779e-02  1.937e-01   0.143 0.885950
## Q106997.fctrYy                    2.839e-01  1.974e-01   1.438 0.150450
## Q107491.fctrNo                    9.113e-02  1.823e-01   0.500 0.617138
## Q107491.fctrYes                   1.526e-01  1.394e-01   1.095 0.273703
## Q107869.fctrNo                    8.067e-03  1.465e-01   0.055 0.956100
## Q107869.fctrYes                  -6.162e-02  1.470e-01  -0.419 0.674990
## `Q108342.fctrIn-person`           2.437e-01  1.758e-01   1.386 0.165798
## Q108342.fctrOnline                3.927e-01  1.859e-01   2.113 0.034628
## Q108343.fctrNo                   -9.699e-02  1.826e-01  -0.531 0.595320
## Q108343.fctrYes                  -1.525e-01  1.928e-01  -0.791 0.429163
## Q108617.fctrNo                    5.212e-02  1.668e-01   0.312 0.754661
## Q108617.fctrYes                  -1.096e-01  2.084e-01  -0.526 0.598981
## Q108754.fctrNo                    8.160e-02  1.880e-01   0.434 0.664277
## Q108754.fctrYes                   8.021e-02  1.967e-01   0.408 0.683502
## Q108855.fctrUmm...               -7.352e-02  2.115e-01  -0.348 0.728086
## `Q108855.fctrYes!`               -1.920e-01  2.077e-01  -0.924 0.355466
## Q108856.fctrSocialize            -1.966e-01  2.144e-01  -0.917 0.359312
## Q108856.fctrSpace                -2.090e-01  1.999e-01  -1.046 0.295615
## Q108950.fctrCautious              1.243e-01  1.569e-01   0.792 0.428176
## `Q108950.fctrRisk-friendly`       2.336e-01  1.680e-01   1.391 0.164215
## Q109244.fctrNo                   -5.639e-01  1.571e-01  -3.590 0.000331
## Q109244.fctrYes                   8.418e-01  2.372e-01   3.548 0.000388
## Q109367.fctrNo                    1.146e-01  1.543e-01   0.743 0.457604
## Q109367.fctrYes                   5.559e-02  1.476e-01   0.377 0.706460
## Q110740.fctrMac                  -4.784e-03  1.305e-01  -0.037 0.970755
## Q110740.fctrPC                   -2.093e-01  1.275e-01  -1.642 0.100552
## Q111220.fctrNo                   -2.921e-02  1.404e-01  -0.208 0.835159
## Q111220.fctrYes                   1.785e-01  1.537e-01   1.162 0.245393
## Q111580.fctrDemanding            -1.205e-02  1.539e-01  -0.078 0.937580
## Q111580.fctrSupportive            1.666e-02  1.444e-01   0.115 0.908122
## Q111848.fctrNo                    9.136e-02  1.521e-01   0.600 0.548187
## Q111848.fctrYes                   1.258e-01  1.472e-01   0.855 0.392552
## Q112270.fctrNo                    1.526e-01  1.426e-01   1.071 0.284353
## Q112270.fctrYes                   1.963e-01  1.426e-01   1.376 0.168822
## Q112478.fctrNo                   -3.537e-01  1.742e-01  -2.031 0.042258
## Q112478.fctrYes                  -1.431e-01  1.683e-01  -0.850 0.395165
## Q112512.fctrNo                    8.850e-02  1.845e-01   0.480 0.631430
## Q112512.fctrYes                   2.717e-02  1.576e-01   0.172 0.863093
## Q113181.fctrNo                    5.457e-02  1.392e-01   0.392 0.694988
## Q113181.fctrYes                  -2.708e-01  1.488e-01  -1.820 0.068738
## Q113583.fctrTalk                  8.673e-02  1.984e-01   0.437 0.662028
## Q113583.fctrTunes                 1.196e-01  1.905e-01   0.628 0.530126
## Q113584.fctrPeople               -1.329e-01  1.948e-01  -0.682 0.495008
## Q113584.fctrTechnology           -1.067e-01  1.936e-01  -0.551 0.581743
## Q113992.fctrNo                    1.935e-01  1.556e-01   1.244 0.213628
## Q113992.fctrYes                   2.856e-01  1.668e-01   1.712 0.086875
## Q114152.fctrNo                   -1.194e-01  1.527e-01  -0.782 0.434021
## Q114152.fctrYes                   2.834e-04  1.638e-01   0.002 0.998620
## Q114386.fctrMysterious            5.544e-02  1.540e-01   0.360 0.718804
## Q114386.fctrTMI                  -1.939e-02  1.575e-01  -0.123 0.902010
## Q114517.fctrNo                    1.992e-01  1.672e-01   1.192 0.233429
## Q114517.fctrYes                   2.268e-01  1.776e-01   1.277 0.201508
## Q114748.fctrNo                   -3.249e-01  1.775e-01  -1.830 0.067248
## Q114748.fctrYes                  -2.991e-01  1.756e-01  -1.704 0.088456
## Q114961.fctrNo                    2.058e-01  1.703e-01   1.209 0.226688
## Q114961.fctrYes                   1.621e-01  1.689e-01   0.960 0.337000
## Q115195.fctrNo                    7.749e-02  1.670e-01   0.464 0.642676
## Q115195.fctrYes                   1.003e-01  1.571e-01   0.639 0.523106
## Q115390.fctrNo                   -2.179e-01  1.507e-01  -1.446 0.148144
## Q115390.fctrYes                   3.199e-03  1.412e-01   0.023 0.981921
## Q115602.fctrNo                    7.157e-02  1.936e-01   0.370 0.711625
## Q115602.fctrYes                   1.700e-01  1.731e-01   0.982 0.325920
## Q115610.fctrNo                   -6.064e-02  2.050e-01  -0.296 0.767409
## Q115610.fctrYes                  -5.365e-02  1.812e-01  -0.296 0.767241
## Q115611.fctrNo                   -1.132e-02  1.927e-01  -0.059 0.953165
## Q115611.fctrYes                  -6.354e-01  2.051e-01  -3.098 0.001948
## Q115777.fctrEnd                   2.219e-02  1.608e-01   0.138 0.890290
## Q115777.fctrStart                 7.381e-02  1.569e-01   0.470 0.638089
## Q115899.fctrCs                    1.997e-01  1.584e-01   1.261 0.207325
## Q115899.fctrMe                    1.439e-02  1.562e-01   0.092 0.926600
## Q116197.fctrA.M.                 -3.837e-01  1.571e-01  -2.443 0.014564
## Q116197.fctrP.M.                 -2.710e-01  1.465e-01  -1.850 0.064307
## Q116441.fctrNo                   -1.787e-01  1.775e-01  -1.007 0.314038
## Q116441.fctrYes                  -1.141e-01  1.907e-01  -0.598 0.549630
## Q116448.fctrNo                    1.792e-01  1.678e-01   1.068 0.285392
## Q116448.fctrYes                   1.342e-01  1.694e-01   0.792 0.428176
## Q116601.fctrNo                    2.378e-01  1.969e-01   1.208 0.227182
## Q116601.fctrYes                   2.011e-01  1.684e-01   1.194 0.232560
## Q116797.fctrNo                   -1.486e-01  1.700e-01  -0.874 0.381878
## Q116797.fctrYes                  -1.989e-01  1.753e-01  -1.135 0.256527
## Q116881.fctrHappy                 1.378e-01  1.654e-01   0.833 0.404703
## Q116881.fctrRight                -1.910e-01  1.806e-01  -1.058 0.290042
## Q116953.fctrNo                    1.438e-02  1.777e-01   0.081 0.935520
## Q116953.fctrYes                   2.519e-01  1.674e-01   1.505 0.132366
## `Q117186.fctrCool headed`         5.909e-04  1.651e-01   0.004 0.997145
## `Q117186.fctrHot headed`         -8.191e-02  1.734e-01  -0.472 0.636613
## `Q117193.fctrOdd hours`           2.161e-02  1.622e-01   0.133 0.894024
## `Q117193.fctrStandard hours`     -5.250e-02  1.545e-01  -0.340 0.733955
## Q118117.fctrNo                   -4.611e-02  1.494e-01  -0.309 0.757609
## Q118117.fctrYes                  -1.600e-02  1.515e-01  -0.106 0.915906
## Q118232.fctrId                    4.334e-01  1.475e-01   2.938 0.003300
## Q118232.fctrPr                    2.416e-01  1.459e-01   1.656 0.097744
## Q118233.fctrNo                   -1.388e-01  1.873e-01  -0.741 0.458554
## Q118233.fctrYes                   2.592e-02  2.032e-01   0.128 0.898489
## Q118237.fctrNo                   -1.491e-01  1.901e-01  -0.784 0.432860
## Q118237.fctrYes                  -1.228e-01  1.870e-01  -0.657 0.511352
## Q118892.fctrNo                    8.175e-02  1.328e-01   0.616 0.538002
## Q118892.fctrYes                   6.557e-02  1.253e-01   0.523 0.600777
## Q119334.fctrNo                   -1.324e-01  1.370e-01  -0.966 0.334101
## Q119334.fctrYes                  -1.128e-01  1.337e-01  -0.844 0.398895
## Q119650.fctrGiving               -1.229e-01  1.418e-01  -0.866 0.386277
## Q119650.fctrReceiving            -1.457e-02  1.587e-01  -0.092 0.926849
## Q119851.fctrNo                   -1.934e-01  1.636e-01  -1.183 0.236959
## Q119851.fctrYes                  -3.755e-02  1.628e-01  -0.231 0.817601
## Q120012.fctrNo                    6.913e-02  1.626e-01   0.425 0.670679
## Q120012.fctrYes                   1.645e-01  1.613e-01   1.020 0.307730
## Q120014.fctrNo                    2.147e-02  1.513e-01   0.142 0.887174
## Q120014.fctrYes                  -1.167e-01  1.437e-01  -0.812 0.416711
## `Q120194.fctrStudy first`         3.247e-01  1.396e-01   2.327 0.019991
## `Q120194.fctrTry first`           2.213e-01  1.449e-01   1.527 0.126690
## Q120379.fctrNo                   -7.067e-02  1.527e-01  -0.463 0.643413
## Q120379.fctrYes                   2.233e-01  1.511e-01   1.477 0.139605
## Q120472.fctrArt                  -9.046e-02  1.564e-01  -0.578 0.563133
## Q120472.fctrScience              -1.605e-01  1.462e-01  -1.098 0.272327
## Q120650.fctrNo                   -7.298e-02  1.973e-01  -0.370 0.711544
## Q120650.fctrYes                  -1.930e-01  1.450e-01  -1.331 0.183119
## Q120978.fctrNo                    5.447e-02  1.588e-01   0.343 0.731664
## Q120978.fctrYes                   5.831e-02  1.550e-01   0.376 0.706728
## Q121011.fctrNo                    1.620e-01  1.591e-01   1.019 0.308384
## Q121011.fctrYes                   1.420e-01  1.567e-01   0.906 0.364743
## Q121699.fctrNo                    3.904e-01  2.441e-01   1.599 0.109749
## Q121699.fctrYes                   5.777e-01  2.352e-01   2.457 0.014024
## Q121700.fctrNo                   -4.448e-01  2.373e-01  -1.875 0.060855
## Q121700.fctrYes                  -3.633e-01  2.560e-01  -1.419 0.155911
## Q122120.fctrNo                   -5.497e-02  1.388e-01  -0.396 0.691973
## Q122120.fctrYes                  -1.399e-01  1.525e-01  -0.918 0.358849
## Q122769.fctrNo                   -6.683e-02  2.114e-01  -0.316 0.751874
## Q122769.fctrYes                  -6.329e-02  2.145e-01  -0.295 0.767949
## Q122770.fctrNo                    1.684e-01  2.569e-01   0.656 0.512118
## Q122770.fctrYes                   1.516e-01  2.536e-01   0.598 0.549881
## Q122771.fctrPc                   -2.170e-01  2.356e-01  -0.921 0.357004
## Q122771.fctrPt                   -4.349e-01  2.496e-01  -1.743 0.081405
## Q123464.fctrNo                   -5.972e-02  1.601e-01  -0.373 0.709091
## Q123464.fctrYes                   8.735e-02  2.338e-01   0.374 0.708690
## Q123621.fctrNo                   -4.738e-02  1.657e-01  -0.286 0.774858
## Q123621.fctrYes                  -1.367e-02  1.703e-01  -0.080 0.936028
## Q124122.fctrNo                   -5.993e-02  1.368e-01  -0.438 0.661365
## Q124122.fctrYes                   9.289e-02  1.313e-01   0.708 0.479156
## Q124742.fctrNo                    1.671e-01  1.049e-01   1.592 0.111319
## Q124742.fctrYes                   3.620e-03  1.212e-01   0.030 0.976181
## Q96024.fctrNo                     9.544e-02  1.337e-01   0.714 0.475449
## Q96024.fctrYes                    2.020e-02  1.246e-01   0.162 0.871276
## `Q98059.fctrOnly-child`           1.759e-02  2.491e-01   0.071 0.943714
## Q98059.fctrYes                    3.089e-01  2.083e-01   1.482 0.138211
## Q98078.fctrNo                    -9.421e-02  1.928e-01  -0.489 0.625147
## Q98078.fctrYes                   -1.409e-01  1.954e-01  -0.721 0.470688
## Q98197.fctrNo                     4.002e-01  1.882e-01   2.126 0.033476
## Q98197.fctrYes                    1.504e-02  1.938e-01   0.078 0.938130
## Q98578.fctrNo                    -4.042e-01  1.562e-01  -2.587 0.009677
## Q98578.fctrYes                   -2.883e-01  1.632e-01  -1.767 0.077225
## Q98869.fctrNo                     4.964e-01  1.699e-01   2.922 0.003476
## Q98869.fctrYes                    6.787e-02  1.436e-01   0.472 0.636582
## Q99480.fctrNo                     1.776e-01  2.038e-01   0.871 0.383694
## Q99480.fctrYes                   -1.198e-01  1.865e-01  -0.642 0.520677
## Q99581.fctrNo                    -2.344e-01  2.019e-01  -1.161 0.245689
## Q99581.fctrYes                   -1.839e-01  2.291e-01  -0.803 0.422065
## Q99716.fctrNo                     2.800e-01  1.740e-01   1.609 0.107676
## Q99716.fctrYes                    1.935e-01  2.233e-01   0.867 0.386029
## `Q99982.fctrCheck!`              -2.407e-01  1.940e-01  -1.241 0.214691
## Q99982.fctrNope                  -1.561e-01  1.970e-01  -0.792 0.428151
## YOB.Age.fctr.L                    5.055e-01  1.913e-01   2.642 0.008241
## YOB.Age.fctr.Q                    2.590e-01  1.578e-01   1.641 0.100848
## YOB.Age.fctr.C                   -5.540e-02  1.366e-01  -0.405 0.685131
## `YOB.Age.fctr^4`                  2.474e-01  1.278e-01   1.935 0.052937
## `YOB.Age.fctr^5`                  7.936e-02  1.180e-01   0.673 0.501237
## `YOB.Age.fctr^6`                  1.448e-01  1.063e-01   1.362 0.173253
## `YOB.Age.fctr^7`                 -1.790e-01  1.011e-01  -1.771 0.076631
## `YOB.Age.fctr^8`                 -2.115e-01  1.042e-01  -2.031 0.042305
## `Hhold.fctrN:.clusterid.fctr2`    1.783e-01  2.915e-01   0.612 0.540671
## `Hhold.fctrMKn:.clusterid.fctr2`  2.522e-01  3.259e-01   0.774 0.439042
## `Hhold.fctrMKy:.clusterid.fctr2`  2.797e-01  1.758e-01   1.591 0.111673
## `Hhold.fctrPKn:.clusterid.fctr2`  5.796e-01  7.391e-01   0.784 0.432919
## `Hhold.fctrPKy:.clusterid.fctr2` -9.746e-01  7.109e-01  -1.371 0.170372
## `Hhold.fctrSKn:.clusterid.fctr2` -2.274e-01  1.323e-01  -1.720 0.085474
## `Hhold.fctrSKy:.clusterid.fctr2`  4.586e-02  4.493e-01   0.102 0.918716
## `Hhold.fctrN:.clusterid.fctr3`    8.939e-02  3.338e-01   0.268 0.788886
## `Hhold.fctrMKn:.clusterid.fctr3` -1.851e-02  2.856e-01  -0.065 0.948311
## `Hhold.fctrMKy:.clusterid.fctr3` -1.838e-02  2.546e-01  -0.072 0.942457
## `Hhold.fctrPKn:.clusterid.fctr3`  2.491e-01  5.516e-01   0.452 0.651602
## `Hhold.fctrPKy:.clusterid.fctr3`  4.135e-02  8.089e-01   0.051 0.959230
## `Hhold.fctrSKn:.clusterid.fctr3` -1.214e-01  2.101e-01  -0.578 0.563557
## `Hhold.fctrSKy:.clusterid.fctr3` -2.643e-01  5.159e-01  -0.512 0.608459
## `Hhold.fctrN:.clusterid.fctr4`           NA         NA      NA       NA
## `Hhold.fctrMKn:.clusterid.fctr4`  2.715e-02  3.377e-01   0.080 0.935929
## `Hhold.fctrMKy:.clusterid.fctr4`         NA         NA      NA       NA
## `Hhold.fctrPKn:.clusterid.fctr4`  1.856e-01  5.501e-01   0.337 0.735789
## `Hhold.fctrPKy:.clusterid.fctr4`         NA         NA      NA       NA
## `Hhold.fctrSKn:.clusterid.fctr4`         NA         NA      NA       NA
## `Hhold.fctrSKy:.clusterid.fctr4`         NA         NA      NA       NA
##                                     
## (Intercept)                         
## .rnorm                              
## Edn.fctr.L                          
## Edn.fctr.Q                          
## Edn.fctr.C                          
## `Edn.fctr^4`                     ** 
## `Edn.fctr^5`                        
## `Edn.fctr^6`                        
## `Edn.fctr^7`                        
## Gender.fctrF                        
## Gender.fctrM                     .  
## Hhold.fctrMKn                       
## Hhold.fctrMKy                       
## Hhold.fctrPKn                    .  
## Hhold.fctrPKy                       
## Hhold.fctrSKn                    .  
## Hhold.fctrSKy                       
## Income.fctr.L                       
## Income.fctr.Q                    .  
## Income.fctr.C                    ** 
## `Income.fctr^4`                     
## `Income.fctr^5`                     
## `Income.fctr^6`                     
## Q100010.fctrNo                      
## Q100010.fctrYes                     
## Q100562.fctrNo                      
## Q100562.fctrYes                     
## Q100680.fctrNo                      
## Q100680.fctrYes                     
## Q100689.fctrNo                   *  
## Q100689.fctrYes                  ** 
## Q101162.fctrOptimist                
## Q101162.fctrPessimist               
## Q101163.fctrDad                     
## Q101163.fctrMom                     
## Q101596.fctrNo                   ** 
## Q101596.fctrYes                  *  
## Q102089.fctrOwn                     
## Q102089.fctrRent                    
## Q102289.fctrNo                      
## Q102289.fctrYes                     
## Q102674.fctrNo                   .  
## Q102674.fctrYes                  .  
## Q102687.fctrNo                   .  
## Q102687.fctrYes                  *  
## Q102906.fctrNo                      
## Q102906.fctrYes                     
## Q103293.fctrNo                      
## Q103293.fctrYes                     
## Q104996.fctrNo                      
## Q104996.fctrYes                     
## Q105655.fctrNo                      
## Q105655.fctrYes                     
## Q105840.fctrNo                      
## Q105840.fctrYes                     
## Q106042.fctrNo                      
## Q106042.fctrYes                     
## Q106272.fctrNo                      
## Q106272.fctrYes                     
## Q106388.fctrNo                      
## Q106388.fctrYes                     
## Q106389.fctrNo                      
## Q106389.fctrYes                     
## Q106993.fctrNo                      
## Q106993.fctrYes                     
## Q106997.fctrGr                      
## Q106997.fctrYy                      
## Q107491.fctrNo                      
## Q107491.fctrYes                     
## Q107869.fctrNo                      
## Q107869.fctrYes                     
## `Q108342.fctrIn-person`             
## Q108342.fctrOnline               *  
## Q108343.fctrNo                      
## Q108343.fctrYes                     
## Q108617.fctrNo                      
## Q108617.fctrYes                     
## Q108754.fctrNo                      
## Q108754.fctrYes                     
## Q108855.fctrUmm...                  
## `Q108855.fctrYes!`                  
## Q108856.fctrSocialize               
## Q108856.fctrSpace                   
## Q108950.fctrCautious                
## `Q108950.fctrRisk-friendly`         
## Q109244.fctrNo                   ***
## Q109244.fctrYes                  ***
## Q109367.fctrNo                      
## Q109367.fctrYes                     
## Q110740.fctrMac                     
## Q110740.fctrPC                      
## Q111220.fctrNo                      
## Q111220.fctrYes                     
## Q111580.fctrDemanding               
## Q111580.fctrSupportive              
## Q111848.fctrNo                      
## Q111848.fctrYes                     
## Q112270.fctrNo                      
## Q112270.fctrYes                     
## Q112478.fctrNo                   *  
## Q112478.fctrYes                     
## Q112512.fctrNo                      
## Q112512.fctrYes                     
## Q113181.fctrNo                      
## Q113181.fctrYes                  .  
## Q113583.fctrTalk                    
## Q113583.fctrTunes                   
## Q113584.fctrPeople                  
## Q113584.fctrTechnology              
## Q113992.fctrNo                      
## Q113992.fctrYes                  .  
## Q114152.fctrNo                      
## Q114152.fctrYes                     
## Q114386.fctrMysterious              
## Q114386.fctrTMI                     
## Q114517.fctrNo                      
## Q114517.fctrYes                     
## Q114748.fctrNo                   .  
## Q114748.fctrYes                  .  
## Q114961.fctrNo                      
## Q114961.fctrYes                     
## Q115195.fctrNo                      
## Q115195.fctrYes                     
## Q115390.fctrNo                      
## Q115390.fctrYes                     
## Q115602.fctrNo                      
## Q115602.fctrYes                     
## Q115610.fctrNo                      
## Q115610.fctrYes                     
## Q115611.fctrNo                      
## Q115611.fctrYes                  ** 
## Q115777.fctrEnd                     
## Q115777.fctrStart                   
## Q115899.fctrCs                      
## Q115899.fctrMe                      
## Q116197.fctrA.M.                 *  
## Q116197.fctrP.M.                 .  
## Q116441.fctrNo                      
## Q116441.fctrYes                     
## Q116448.fctrNo                      
## Q116448.fctrYes                     
## Q116601.fctrNo                      
## Q116601.fctrYes                     
## Q116797.fctrNo                      
## Q116797.fctrYes                     
## Q116881.fctrHappy                   
## Q116881.fctrRight                   
## Q116953.fctrNo                      
## Q116953.fctrYes                     
## `Q117186.fctrCool headed`           
## `Q117186.fctrHot headed`            
## `Q117193.fctrOdd hours`             
## `Q117193.fctrStandard hours`        
## Q118117.fctrNo                      
## Q118117.fctrYes                     
## Q118232.fctrId                   ** 
## Q118232.fctrPr                   .  
## Q118233.fctrNo                      
## Q118233.fctrYes                     
## Q118237.fctrNo                      
## Q118237.fctrYes                     
## Q118892.fctrNo                      
## Q118892.fctrYes                     
## Q119334.fctrNo                      
## Q119334.fctrYes                     
## Q119650.fctrGiving                  
## Q119650.fctrReceiving               
## Q119851.fctrNo                      
## Q119851.fctrYes                     
## Q120012.fctrNo                      
## Q120012.fctrYes                     
## Q120014.fctrNo                      
## Q120014.fctrYes                     
## `Q120194.fctrStudy first`        *  
## `Q120194.fctrTry first`             
## Q120379.fctrNo                      
## Q120379.fctrYes                     
## Q120472.fctrArt                     
## Q120472.fctrScience                 
## Q120650.fctrNo                      
## Q120650.fctrYes                     
## Q120978.fctrNo                      
## Q120978.fctrYes                     
## Q121011.fctrNo                      
## Q121011.fctrYes                     
## Q121699.fctrNo                      
## Q121699.fctrYes                  *  
## Q121700.fctrNo                   .  
## Q121700.fctrYes                     
## Q122120.fctrNo                      
## Q122120.fctrYes                     
## Q122769.fctrNo                      
## Q122769.fctrYes                     
## Q122770.fctrNo                      
## Q122770.fctrYes                     
## Q122771.fctrPc                      
## Q122771.fctrPt                   .  
## Q123464.fctrNo                      
## Q123464.fctrYes                     
## Q123621.fctrNo                      
## Q123621.fctrYes                     
## Q124122.fctrNo                      
## Q124122.fctrYes                     
## Q124742.fctrNo                      
## Q124742.fctrYes                     
## Q96024.fctrNo                       
## Q96024.fctrYes                      
## `Q98059.fctrOnly-child`             
## Q98059.fctrYes                      
## Q98078.fctrNo                       
## Q98078.fctrYes                      
## Q98197.fctrNo                    *  
## Q98197.fctrYes                      
## Q98578.fctrNo                    ** 
## Q98578.fctrYes                   .  
## Q98869.fctrNo                    ** 
## Q98869.fctrYes                      
## Q99480.fctrNo                       
## Q99480.fctrYes                      
## Q99581.fctrNo                       
## Q99581.fctrYes                      
## Q99716.fctrNo                       
## Q99716.fctrYes                      
## `Q99982.fctrCheck!`                 
## Q99982.fctrNope                     
## YOB.Age.fctr.L                   ** 
## YOB.Age.fctr.Q                      
## YOB.Age.fctr.C                      
## `YOB.Age.fctr^4`                 .  
## `YOB.Age.fctr^5`                    
## `YOB.Age.fctr^6`                    
## `YOB.Age.fctr^7`                 .  
## `YOB.Age.fctr^8`                 *  
## `Hhold.fctrN:.clusterid.fctr2`      
## `Hhold.fctrMKn:.clusterid.fctr2`    
## `Hhold.fctrMKy:.clusterid.fctr2`    
## `Hhold.fctrPKn:.clusterid.fctr2`    
## `Hhold.fctrPKy:.clusterid.fctr2`    
## `Hhold.fctrSKn:.clusterid.fctr2` .  
## `Hhold.fctrSKy:.clusterid.fctr2`    
## `Hhold.fctrN:.clusterid.fctr3`      
## `Hhold.fctrMKn:.clusterid.fctr3`    
## `Hhold.fctrMKy:.clusterid.fctr3`    
## `Hhold.fctrPKn:.clusterid.fctr3`    
## `Hhold.fctrPKy:.clusterid.fctr3`    
## `Hhold.fctrSKn:.clusterid.fctr3`    
## `Hhold.fctrSKy:.clusterid.fctr3`    
## `Hhold.fctrN:.clusterid.fctr4`      
## `Hhold.fctrMKn:.clusterid.fctr4`    
## `Hhold.fctrMKy:.clusterid.fctr4`    
## `Hhold.fctrPKn:.clusterid.fctr4`    
## `Hhold.fctrPKy:.clusterid.fctr4`    
## `Hhold.fctrSKn:.clusterid.fctr4`    
## `Hhold.fctrSKy:.clusterid.fctr4`    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 6150.3  on 4447  degrees of freedom
## Residual deviance: 5314.8  on 4199  degrees of freedom
## AIC: 5812.8
## 
## Number of Fisher Scoring iterations: 5
## 
## [1] "myfit_mdl: train diagnostics complete: 21.899000 secs"

## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading

## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading

##          Prediction
## Reference    R    D
##         R 1834  257
##         D 1365  992
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   6.353417e-01   2.893156e-01   6.209988e-01   6.495067e-01   5.299011e-01 
## AccuracyPValue  McnemarPValue 
##   5.398747e-46  2.533704e-166

## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading

## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading

##          Prediction
## Reference   R   D
##         R 484  42
##         D 441 153
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   5.687500e-01   1.703322e-01   5.391503e-01   5.979877e-01   5.303571e-01 
## AccuracyPValue  McnemarPValue 
##   5.402616e-03   2.675549e-73 
## [1] "myfit_mdl: predict complete: 32.973000 secs"
##               id
## 1 All.X##rcv#glm
##                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               feats
## 1 Q109244.fctr,Hhold.fctr,Edn.fctr,Q101163.fctr,Q100689.fctr,Q98078.fctr,Q99716.fctr,Q120379.fctr,Q121699.fctr,Q105840.fctr,Q113583.fctr,Q115195.fctr,Q102089.fctr,Q98059.fctr,Q114386.fctr,Q100680.fctr,Q108342.fctr,Q111848.fctr,YOB.Age.fctr,Q118892.fctr,Q102687.fctr,Q115390.fctr,Q119851.fctr,Q114517.fctr,Q120012.fctr,Q109367.fctr,Q114961.fctr,Q121700.fctr,Q124122.fctr,Q111220.fctr,Q113992.fctr,Q121011.fctr,Q106042.fctr,Q116448.fctr,Q116601.fctr,Q104996.fctr,Q102906.fctr,Q113584.fctr,Q108950.fctr,Q102674.fctr,Q103293.fctr,Q112478.fctr,Q114748.fctr,Q107491.fctr,Q100562.fctr,Q108617.fctr,Q100010.fctr,Q115602.fctr,Q116953.fctr,Q115610.fctr,Q106997.fctr,Q120978.fctr,Q112512.fctr,Q108343.fctr,Q96024.fctr,Q106389.fctr,.rnorm,Q108754.fctr,Q98578.fctr,Q101162.fctr,Q115777.fctr,Q99581.fctr,Q124742.fctr,Q116797.fctr,Q112270.fctr,Q118237.fctr,Q119650.fctr,Q111580.fctr,Q123464.fctr,Q117193.fctr,Q99982.fctr,Q108856.fctr,Q118233.fctr,Q102289.fctr,Q116197.fctr,Income.fctr,Q118232.fctr,Q120194.fctr,Q114152.fctr,Q122770.fctr,Q117186.fctr,Q105655.fctr,Q106993.fctr,Q119334.fctr,Q122120.fctr,Q116441.fctr,Q118117.fctr,Q123621.fctr,Q122769.fctr,Q120650.fctr,Q98869.fctr,Q107869.fctr,Q120014.fctr,Q115899.fctr,Q106388.fctr,Q99480.fctr,Q122771.fctr,Q108855.fctr,Q110740.fctr,Q106272.fctr,Q101596.fctr,Q116881.fctr,Q120472.fctr,Q98197.fctr,Q113181.fctr,Q115611.fctr,Gender.fctr,Hhold.fctr:.clusterid.fctr
##   max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1               1                     18.864                 1.915
##   max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1       0.6709241    0.6456241     0.696224       0.2603537
##   opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1                   0.65       0.6933837        0.6013205
##   max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1             0.6209988             0.6495067     0.1993351
##   max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1        0.593492    0.5304183    0.6565657       0.3391328
##   opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1                   0.75       0.6671261          0.56875
##   max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1             0.5391503             0.5979877     0.1703322
##   max.AccuracySD.fit max.KappaSD.fit
## 1         0.01004923      0.02079105
## [1] "myfit_mdl: exit: 32.989000 secs"

# Check if other preProcess methods improve model performance
fit.models_1_chunk_df <- 
    myadd_chunk(fit.models_1_chunk_df, "fit.models_1_preProc", major.inc = FALSE,
                label.minor = "preProc")

##                  label step_major step_minor label_minor     bgn     end
## 4   fit.models_1_All.X          1          3         glm 383.698 416.742
## 5 fit.models_1_preProc          1          4     preProc 416.742      NA
##   elapsed
## 4  33.044
## 5      NA

require(gdata)

## Loading required package: gdata

## gdata: read.xls support for 'XLS' (Excel 97-2004) files ENABLED.

## 

## gdata: read.xls support for 'XLSX' (Excel 2007+) files ENABLED.

## 
## Attaching package: 'gdata'

## The following objects are masked from 'package:dplyr':
## 
##     combine, first, last

## The following object is masked from 'package:stats':
## 
##     nobs

## The following object is masked from 'package:utils':
## 
##     object.size

mdl_id <- orderBy(get_model_sel_frmla(), glb_models_df)[1, "id"]
indepVar <- trim(unlist(strsplit(glb_models_df[glb_models_df$id == mdl_id,
                                                      "feats"], "[,]")))
method <- tail(unlist(strsplit(mdl_id, "[.]")), 1)
mdl_id_pfx <- paste0(head(unlist(strsplit(mdl_id, "[.]")), -1), collapse = ".")
if (!is.null(glbObsFitOutliers[[mdl_id_pfx]])) {
    fitobs_df <- glbObsFit[!(glbObsFit[, glbFeatsId] %in%
                                     glbObsFitOutliers[[mdl_id_pfx]]), ]
        print(sprintf("Outliers removed: %d", nrow(glbObsFit) - nrow(fitobs_df)))
        print(setdiff(glbObsFit[, glbFeatsId], fitobs_df[, glbFeatsId]))
    
} else fitobs_df <- glbObsFit

for (prePr in glb_preproc_methods) {   
    # The operations are applied in this order: 
    #   Box-Cox/Yeo-Johnson transformation, centering, scaling, range, imputation, PCA, ICA then spatial sign.
    
    ret_lst <- myfit_mdl(mdl_specs_lst=myinit_mdl_specs_lst(mdl_specs_lst=list(
            id.prefix=mdl_id_pfx, 
            type=glb_model_type, tune.df=glbMdlTuneParams,
            trainControl.method="repeatedcv",
            trainControl.number=glb_rcv_n_folds,
            trainControl.repeats=glb_rcv_n_repeats,
            trainControl.classProbs = glb_is_classification,
            trainControl.summaryFunction = glbMdlMetricSummaryFn,
            train.metric = glbMdlMetricSummary, 
            train.maximize = glbMdlMetricMaximize,    
            train.method=method, train.preProcess=prePr)),
            indepVar=indepVar, rsp_var=glb_rsp_var, 
            fit_df=fitobs_df, OOB_df=glbObsOOB)
}            
    
    # If (All|RFE).X.glm is less accurate than Low.Cor.X.glm
    #   check NA coefficients & filter appropriate terms in indepVar
#     if (method == "glm") {
#         orig_glm <- glb_models_lst[[paste0(mdl_id, ".", model_method)]]$finalModel
#         orig_glm <- glb_models_lst[["All.X.glm"]]$finalModel; print(summary(orig_glm))
#         orig_glm <- glb_models_lst[["RFE.X.glm"]]$finalModel; print(summary(orig_glm))
#           require(car)
#           vif_orig_glm <- vif(orig_glm); print(vif_orig_glm)
#           # if vif errors out with "there are aliased coefficients in the model"
#               alias_orig_glm <- alias(orig_glm); alias_complete_orig_glm <- (alias_orig_glm$Complete > 0); alias_complete_orig_glm <- alias_complete_orig_glm[rowSums(alias_complete_orig_glm) > 0, colSums(alias_complete_orig_glm) > 0]; print(alias_complete_orig_glm)
#           print(vif_orig_glm[!is.na(vif_orig_glm) & (vif_orig_glm == Inf)])
#           print(which.max(vif_orig_glm))
#           print(sort(vif_orig_glm[vif_orig_glm >= 1.0e+03], decreasing=TRUE))
#           glbObsFit[c(1143, 3637, 3953, 4105), c("UniqueID", "Popular", "H.P.quandary", "Headline")]
#           glb_feats_df[glb_feats_df$id %in% grep("[HSA]\\.chrs.n.log", glb_feats_df$id, value=TRUE) | glb_feats_df$cor.high.X %in%    grep("[HSA]\\.chrs.n.log", glb_feats_df$id, value=TRUE), ]
#           all.equal(glbObsAll$S.chrs.uppr.n.log, glbObsAll$A.chrs.uppr.n.log)
#           cor(glbObsAll$S.T.herald, glbObsAll$S.T.tribun)
#           mydspObs(Abstract.contains="[Dd]iar", cols=("Abstract"), all=TRUE)
#           subset(glb_feats_df, cor.y.abs <= glb_feats_df[glb_feats_df$id == ".rnorm", "cor.y.abs"])
#         corxx_mtrx <- cor(data.matrix(glbObsAll[, setdiff(names(glbObsAll), myfind_chr_cols_df(glbObsAll))]), use="pairwise.complete.obs"); abs_corxx_mtrx <- abs(corxx_mtrx); diag(abs_corxx_mtrx) <- 0
#           which.max(abs_corxx_mtrx["S.T.tribun", ])
#           abs_corxx_mtrx["A.npnct08.log", "S.npnct08.log"]
#         step_glm <- step(orig_glm)
#     }
    # Since caret does not optimize rpart well
#     if (method == "rpart")
#         ret_lst <- myfit_mdl(mdl_id=paste0(mdl_id_pfx, ".cp.0"), model_method=method,
#                                 indepVar=indepVar,
#                                 model_type=glb_model_type,
#                                 rsp_var=glb_rsp_var,
#                                 fit_df=glbObsFit, OOB_df=glbObsOOB,        
#             n_cv_folds=0, tune_models_df=data.frame(parameter="cp", min=0.0, max=0.0, by=0.1))

# User specified
#   Ensure at least 2 vars in each regression; else varImp crashes
# sav_models_lst <- glb_models_lst; sav_models_df <- glb_models_df; sav_featsimp_df <- glb_featsimp_df; all.equal(sav_featsimp_df, glb_featsimp_df)
# glb_models_lst <- sav_models_lst; glb_models_df <- sav_models_df; glm_featsimp_df <- sav_featsimp_df

    # easier to exclude features
# require(gdata) # needed for trim
# mdl_id <- "";
# indepVar <- head(subset(glb_models_df, grepl("All\\.X\\.", mdl_id), select=feats)
#                         , 1)[, "feats"]
# indepVar <- trim(unlist(strsplit(indepVar, "[,]")))
# indepVar <- setdiff(indepVar, ".rnorm")

    # easier to include features
#stop(here"); sav_models_df <- glb_models_df; glb_models_df <- sav_models_df
# !_sp
# mdl_id <- "csm"; indepVar <- c(NULL
#     ,"prdline.my.fctr", "prdline.my.fctr:.clusterid.fctr"
#     ,"prdline.my.fctr*biddable"
#     #,"prdline.my.fctr*startprice.log"
#     #,"prdline.my.fctr*startprice.diff"    
#     ,"prdline.my.fctr*condition.fctr"
#     ,"prdline.my.fctr*D.terms.post.stop.n"
#     #,"prdline.my.fctr*D.terms.post.stem.n"
#     ,"prdline.my.fctr*cellular.fctr"    
# #    ,"<feat1>:<feat2>"
#                                            )
# for (method in glbMdlMethods) {
#     ret_lst <- myfit_mdl(mdl_id=mdl_id, model_method=method,
#                                 indepVar=indepVar,
#                                 model_type=glb_model_type,
#                                 rsp_var=glb_rsp_var,
#                                 fit_df=glbObsFit, OOB_df=glbObsOOB,
#                     n_cv_folds=glb_rcv_n_folds, tune_models_df=glbMdlTuneParams)
#     csm_mdl_id <- paste0(mdl_id, ".", method)
#     csm_featsimp_df <- myget_feats_importance(glb_models_lst[[paste0(mdl_id, ".",
#                                                                      method)]]);               print(head(csm_featsimp_df))
# }
###

# Ntv.1.lm <- lm(reformulate(indepVar, glb_rsp_var), glbObsTrn); print(summary(Ntv.1.lm))

#glb_models_df[, "max.Accuracy.OOB", FALSE]
#varImp(glb_models_lst[["Low.cor.X.glm"]])
#orderBy(~ -Overall, varImp(glb_models_lst[["All.X.2.glm"]])$imp)
#orderBy(~ -Overall, varImp(glb_models_lst[["All.X.3.glm"]])$imp)
#glb_feats_df[grepl("npnct28", glb_feats_df$id), ]

    # User specified bivariate models
#     indepVar_lst <- list()
#     for (feat in setdiff(names(glbObsFit), 
#                          union(glb_rsp_var, glbFeatsExclude)))
#         indepVar_lst[["feat"]] <- feat

    # User specified combinatorial models
#     indepVar_lst <- list()
#     combn_mtrx <- combn(c("<feat1_name>", "<feat2_name>", "<featn_name>"), 
#                           <num_feats_to_choose>)
#     for (combn_ix in 1:ncol(combn_mtrx))
#         #print(combn_mtrx[, combn_ix])
#         indepVar_lst[[combn_ix]] <- combn_mtrx[, combn_ix]
    
    # template for myfit_mdl
    #   rf is hard-coded in caret to recognize only Accuracy / Kappa evaluation metrics
    #       only for OOB in trainControl ?
    
#     ret_lst <- myfit_mdl_fn(mdl_id=paste0(mdl_id_pfx, ""), model_method=method,
#                             indepVar=indepVar,
#                             rsp_var=glb_rsp_var,
#                             fit_df=glbObsFit, OOB_df=glbObsOOB,
#                             n_cv_folds=glb_rcv_n_folds, tune_models_df=glbMdlTuneParams,
#                             model_loss_mtrx=glbMdlMetric_terms,
#                             model_summaryFunction=glbMdlMetricSummaryFn,
#                             model_metric=glbMdlMetricSummary,
#                             model_metric_maximize=glbMdlMetricMaximize)

# Simplify a model
# fit_df <- glbObsFit; glb_mdl <- step(<complex>_mdl)

# Non-caret models
#     rpart_area_mdl <- rpart(reformulate("Area", response=glb_rsp_var), 
#                                data=glbObsFit, #method="class", 
#                                control=rpart.control(cp=0.12),
#                            parms=list(loss=glbMdlMetric_terms))
#     print("rpart_sel_wlm_mdl"); prp(rpart_sel_wlm_mdl)
# 

print(glb_models_df)

##                                                              id
## MFO###myMFO_classfr                         MFO###myMFO_classfr
## Random###myrandom_classfr             Random###myrandom_classfr
## Max.cor.Y.rcv.1X1###glmnet           Max.cor.Y.rcv.1X1###glmnet
## Max.cor.Y##rcv#rpart                       Max.cor.Y##rcv#rpart
## Interact.High.cor.Y##rcv#glmnet Interact.High.cor.Y##rcv#glmnet
## Low.cor.X##rcv#glmnet                     Low.cor.X##rcv#glmnet
## All.X##rcv#glmnet                             All.X##rcv#glmnet
## All.X##rcv#glm                                   All.X##rcv#glm
##                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             feats
## MFO###myMFO_classfr                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        .rnorm
## Random###myrandom_classfr                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  .rnorm
## Max.cor.Y.rcv.1X1###glmnet                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               Q109244.fctr,Gender.fctr
## Max.cor.Y##rcv#rpart                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     Q109244.fctr,Gender.fctr
## Interact.High.cor.Y##rcv#glmnet                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            Q109244.fctr,Gender.fctr,Q109244.fctr:Q99480.fctr,Q109244.fctr:Q98078.fctr,Q109244.fctr:Q100689.fctr,Q109244.fctr:Q108855.fctr,Q109244.fctr:Q123621.fctr,Q109244.fctr:Q122771.fctr,Q109244.fctr:Q120472.fctr,Q109244.fctr:Q106272.fctr
## Low.cor.X##rcv#glmnet           Q109244.fctr,Hhold.fctr,Edn.fctr,Q101163.fctr,Q100689.fctr,Q98078.fctr,Q99716.fctr,Q120379.fctr,Q121699.fctr,Q105840.fctr,Q113583.fctr,Q115195.fctr,Q102089.fctr,Q98059.fctr,Q114386.fctr,Q100680.fctr,Q108342.fctr,Q111848.fctr,YOB.Age.fctr,Q118892.fctr,Q102687.fctr,Q115390.fctr,Q119851.fctr,Q114517.fctr,Q120012.fctr,Q109367.fctr,Q114961.fctr,Q121700.fctr,Q124122.fctr,Q111220.fctr,Q113992.fctr,Q121011.fctr,Q106042.fctr,Q116448.fctr,Q116601.fctr,Q104996.fctr,Q102906.fctr,Q113584.fctr,Q108950.fctr,Q102674.fctr,Q103293.fctr,Q112478.fctr,Q114748.fctr,Q107491.fctr,Q100562.fctr,Q108617.fctr,Q100010.fctr,Q115602.fctr,Q116953.fctr,Q115610.fctr,Q106997.fctr,Q120978.fctr,Q112512.fctr,Q108343.fctr,Q96024.fctr,Q106389.fctr,.rnorm,Q108754.fctr,Q98578.fctr,Q101162.fctr,Q115777.fctr,Q99581.fctr,Q124742.fctr,Q116797.fctr,Q112270.fctr,Q118237.fctr,Q119650.fctr,Q111580.fctr,Q123464.fctr,Q117193.fctr,Q99982.fctr,Q108856.fctr,Q118233.fctr,Q102289.fctr,Q116197.fctr,Income.fctr,Q118232.fctr,Q120194.fctr,Q114152.fctr,Q122770.fctr,Q117186.fctr,Q105655.fctr,Q106993.fctr,Q119334.fctr,Q122120.fctr,Q116441.fctr,Q118117.fctr,Q123621.fctr,Q122769.fctr,Q120650.fctr,Q98869.fctr,Q107869.fctr,Q120014.fctr,Q115899.fctr,Q106388.fctr,Q99480.fctr,Q122771.fctr,Q108855.fctr,Q110740.fctr,Q106272.fctr,Q101596.fctr,Q116881.fctr,Q120472.fctr,Q98197.fctr,Q113181.fctr,Q115611.fctr,Gender.fctr,Hhold.fctr:.clusterid.fctr
## All.X##rcv#glmnet               Q109244.fctr,Hhold.fctr,Edn.fctr,Q101163.fctr,Q100689.fctr,Q98078.fctr,Q99716.fctr,Q120379.fctr,Q121699.fctr,Q105840.fctr,Q113583.fctr,Q115195.fctr,Q102089.fctr,Q98059.fctr,Q114386.fctr,Q100680.fctr,Q108342.fctr,Q111848.fctr,YOB.Age.fctr,Q118892.fctr,Q102687.fctr,Q115390.fctr,Q119851.fctr,Q114517.fctr,Q120012.fctr,Q109367.fctr,Q114961.fctr,Q121700.fctr,Q124122.fctr,Q111220.fctr,Q113992.fctr,Q121011.fctr,Q106042.fctr,Q116448.fctr,Q116601.fctr,Q104996.fctr,Q102906.fctr,Q113584.fctr,Q108950.fctr,Q102674.fctr,Q103293.fctr,Q112478.fctr,Q114748.fctr,Q107491.fctr,Q100562.fctr,Q108617.fctr,Q100010.fctr,Q115602.fctr,Q116953.fctr,Q115610.fctr,Q106997.fctr,Q120978.fctr,Q112512.fctr,Q108343.fctr,Q96024.fctr,Q106389.fctr,.rnorm,Q108754.fctr,Q98578.fctr,Q101162.fctr,Q115777.fctr,Q99581.fctr,Q124742.fctr,Q116797.fctr,Q112270.fctr,Q118237.fctr,Q119650.fctr,Q111580.fctr,Q123464.fctr,Q117193.fctr,Q99982.fctr,Q108856.fctr,Q118233.fctr,Q102289.fctr,Q116197.fctr,Income.fctr,Q118232.fctr,Q120194.fctr,Q114152.fctr,Q122770.fctr,Q117186.fctr,Q105655.fctr,Q106993.fctr,Q119334.fctr,Q122120.fctr,Q116441.fctr,Q118117.fctr,Q123621.fctr,Q122769.fctr,Q120650.fctr,Q98869.fctr,Q107869.fctr,Q120014.fctr,Q115899.fctr,Q106388.fctr,Q99480.fctr,Q122771.fctr,Q108855.fctr,Q110740.fctr,Q106272.fctr,Q101596.fctr,Q116881.fctr,Q120472.fctr,Q98197.fctr,Q113181.fctr,Q115611.fctr,Gender.fctr,Hhold.fctr:.clusterid.fctr
## All.X##rcv#glm                  Q109244.fctr,Hhold.fctr,Edn.fctr,Q101163.fctr,Q100689.fctr,Q98078.fctr,Q99716.fctr,Q120379.fctr,Q121699.fctr,Q105840.fctr,Q113583.fctr,Q115195.fctr,Q102089.fctr,Q98059.fctr,Q114386.fctr,Q100680.fctr,Q108342.fctr,Q111848.fctr,YOB.Age.fctr,Q118892.fctr,Q102687.fctr,Q115390.fctr,Q119851.fctr,Q114517.fctr,Q120012.fctr,Q109367.fctr,Q114961.fctr,Q121700.fctr,Q124122.fctr,Q111220.fctr,Q113992.fctr,Q121011.fctr,Q106042.fctr,Q116448.fctr,Q116601.fctr,Q104996.fctr,Q102906.fctr,Q113584.fctr,Q108950.fctr,Q102674.fctr,Q103293.fctr,Q112478.fctr,Q114748.fctr,Q107491.fctr,Q100562.fctr,Q108617.fctr,Q100010.fctr,Q115602.fctr,Q116953.fctr,Q115610.fctr,Q106997.fctr,Q120978.fctr,Q112512.fctr,Q108343.fctr,Q96024.fctr,Q106389.fctr,.rnorm,Q108754.fctr,Q98578.fctr,Q101162.fctr,Q115777.fctr,Q99581.fctr,Q124742.fctr,Q116797.fctr,Q112270.fctr,Q118237.fctr,Q119650.fctr,Q111580.fctr,Q123464.fctr,Q117193.fctr,Q99982.fctr,Q108856.fctr,Q118233.fctr,Q102289.fctr,Q116197.fctr,Income.fctr,Q118232.fctr,Q120194.fctr,Q114152.fctr,Q122770.fctr,Q117186.fctr,Q105655.fctr,Q106993.fctr,Q119334.fctr,Q122120.fctr,Q116441.fctr,Q118117.fctr,Q123621.fctr,Q122769.fctr,Q120650.fctr,Q98869.fctr,Q107869.fctr,Q120014.fctr,Q115899.fctr,Q106388.fctr,Q99480.fctr,Q122771.fctr,Q108855.fctr,Q110740.fctr,Q106272.fctr,Q101596.fctr,Q116881.fctr,Q120472.fctr,Q98197.fctr,Q113181.fctr,Q115611.fctr,Gender.fctr,Hhold.fctr:.clusterid.fctr
##                                 max.nTuningRuns min.elapsedtime.everything
## MFO###myMFO_classfr                           0                      0.628
## Random###myrandom_classfr                     0                      0.292
## Max.cor.Y.rcv.1X1###glmnet                    0                      0.892
## Max.cor.Y##rcv#rpart                          5                      2.191
## Interact.High.cor.Y##rcv#glmnet              25                      6.609
## Low.cor.X##rcv#glmnet                        25                     24.339
## All.X##rcv#glmnet                            25                     24.094
## All.X##rcv#glm                                1                     18.864
##                                 min.elapsedtime.final max.AUCpROC.fit
## MFO###myMFO_classfr                             0.004       0.5000000
## Random###myrandom_classfr                       0.003       0.4942483
## Max.cor.Y.rcv.1X1###glmnet                      0.064       0.5971118
## Max.cor.Y##rcv#rpart                            0.025       0.5971118
## Interact.High.cor.Y##rcv#glmnet                 0.360       0.6184781
## Low.cor.X##rcv#glmnet                           2.120       0.6273089
## All.X##rcv#glmnet                               2.105       0.6273089
## All.X##rcv#glm                                  1.915       0.6709241
##                                 max.Sens.fit max.Spec.fit max.AUCROCR.fit
## MFO###myMFO_classfr                0.0000000    1.0000000       0.5000000
## Random###myrandom_classfr          0.4619799    0.5265168       0.5073101
## Max.cor.Y.rcv.1X1###glmnet         0.5480631    0.6461604       0.3580613
## Max.cor.Y##rcv#rpart               0.5480631    0.6461604       0.3676308
## Interact.High.cor.Y##rcv#glmnet    0.5958871    0.6410692       0.3319465
## Low.cor.X##rcv#glmnet              0.5155428    0.7390751       0.3048695
## All.X##rcv#glmnet                  0.5155428    0.7390751       0.3048695
## All.X##rcv#glm                     0.6456241    0.6962240       0.2603537
##                                 opt.prob.threshold.fit max.f.score.fit
## MFO###myMFO_classfr                               0.50       0.6395473
## Random###myrandom_classfr                         0.55       0.6395473
## Max.cor.Y.rcv.1X1###glmnet                        0.60       0.6720662
## Max.cor.Y##rcv#rpart                              0.55       0.6720662
## Interact.High.cor.Y##rcv#glmnet                   0.65       0.6727114
## Low.cor.X##rcv#glmnet                             0.60       0.6756231
## All.X##rcv#glmnet                                 0.60       0.6756231
## All.X##rcv#glm                                    0.65       0.6933837
##                                 max.Accuracy.fit max.AccuracyLower.fit
## MFO###myMFO_classfr                    0.4700989             0.4553427
## Random###myrandom_classfr              0.4700989             0.4553427
## Max.cor.Y.rcv.1X1###glmnet             0.5721673             0.5574714
## Max.cor.Y##rcv#rpart                   0.6000450             0.5574714
## Interact.High.cor.Y##rcv#glmnet        0.6058167             0.5633390
## Low.cor.X##rcv#glmnet                  0.6247001             0.5728218
## All.X##rcv#glmnet                      0.6247001             0.5728218
## All.X##rcv#glm                         0.6013205             0.6209988
##                                 max.AccuracyUpper.fit max.Kappa.fit
## MFO###myMFO_classfr                         0.4848945     0.0000000
## Random###myrandom_classfr                   0.4848945     0.0000000
## Max.cor.Y.rcv.1X1###glmnet                  0.5867683     0.1772539
## Max.cor.Y##rcv#rpart                        0.5867683     0.1947896
## Interact.High.cor.Y##rcv#glmnet             0.5925837     0.2088694
## Low.cor.X##rcv#glmnet                       0.6019734     0.2399024
## All.X##rcv#glmnet                           0.6019734     0.2399024
## All.X##rcv#glm                              0.6495067     0.1993351
##                                 max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB
## MFO###myMFO_classfr                   0.5000000    0.0000000    1.0000000
## Random###myrandom_classfr             0.5235690    0.5000000    0.5471380
## Max.cor.Y.rcv.1X1###glmnet            0.5896897    0.5228137    0.6565657
## Max.cor.Y##rcv#rpart                  0.5896897    0.5228137    0.6565657
## Interact.High.cor.Y##rcv#glmnet       0.6031353    0.5665399    0.6397306
## Low.cor.X##rcv#glmnet                 0.6237374    0.5000000    0.7474747
## All.X##rcv#glmnet                     0.6237374    0.5000000    0.7474747
## All.X##rcv#glm                        0.5934920    0.5304183    0.6565657
##                                 max.AUCROCR.OOB opt.prob.threshold.OOB
## MFO###myMFO_classfr                   0.5000000                   0.50
## Random###myrandom_classfr             0.5191202                   0.55
## Max.cor.Y.rcv.1X1###glmnet            0.3658672                   0.60
## Max.cor.Y##rcv#rpart                  0.3774772                   0.55
## Interact.High.cor.Y##rcv#glmnet       0.3571392                   0.65
## Low.cor.X##rcv#glmnet                 0.3184875                   0.70
## All.X##rcv#glmnet                     0.3184875                   0.70
## All.X##rcv#glm                        0.3391328                   0.75
##                                 max.f.score.OOB max.Accuracy.OOB
## MFO###myMFO_classfr                   0.6391252        0.4696429
## Random###myrandom_classfr             0.6391252        0.4696429
## Max.cor.Y.rcv.1X1###glmnet            0.6643789        0.5633929
## Max.cor.Y##rcv#rpart                  0.6643789        0.5633929
## Interact.High.cor.Y##rcv#glmnet       0.6680556        0.5732143
## Low.cor.X##rcv#glmnet                 0.6644250        0.5544643
## All.X##rcv#glmnet                     0.6644250        0.5544643
## All.X##rcv#glm                        0.6671261        0.5687500
##                                 max.AccuracyLower.OOB
## MFO###myMFO_classfr                         0.4400805
## Random###myrandom_classfr                   0.4400805
## Max.cor.Y.rcv.1X1###glmnet                  0.5337655
## Max.cor.Y##rcv#rpart                        0.5337655
## Interact.High.cor.Y##rcv#glmnet             0.5436402
## Low.cor.X##rcv#glmnet                       0.5247985
## All.X##rcv#glmnet                           0.5247985
## All.X##rcv#glm                              0.5391503
##                                 max.AccuracyUpper.OOB max.Kappa.OOB
## MFO###myMFO_classfr                         0.4993651     0.0000000
## Random###myrandom_classfr                   0.4993651     0.0000000
## Max.cor.Y.rcv.1X1###glmnet                  0.5926864     0.1605510
## Max.cor.Y##rcv#rpart                        0.5926864     0.1605510
## Interact.High.cor.Y##rcv#glmnet             0.6024028     0.1779779
## Low.cor.X##rcv#glmnet                       0.5838432     0.1460545
## All.X##rcv#glmnet                           0.5838432     0.1460545
## All.X##rcv#glm                              0.5979877     0.1703322
##                                 max.AccuracySD.fit max.KappaSD.fit
## MFO###myMFO_classfr                             NA              NA
## Random###myrandom_classfr                       NA              NA
## Max.cor.Y.rcv.1X1###glmnet                      NA              NA
## Max.cor.Y##rcv#rpart                   0.012403504      0.02559319
## Interact.High.cor.Y##rcv#glmnet        0.013121299      0.02732571
## Low.cor.X##rcv#glmnet                  0.004682542      0.01138792
## All.X##rcv#glmnet                      0.004682542      0.01138792
## All.X##rcv#glm                         0.010049230      0.02079105

rm(ret_lst)
fit.models_1_chunk_df <- 
    myadd_chunk(fit.models_1_chunk_df, "fit.models_1_end", major.inc = FALSE,
                label.minor = "teardown")

##                  label step_major step_minor label_minor     bgn     end
## 5 fit.models_1_preProc          1          4     preProc 416.742 418.825
## 6     fit.models_1_end          1          5    teardown 418.826      NA
##   elapsed
## 5   2.083
## 6      NA

glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.models", major.inc = FALSE)

##        label step_major step_minor label_minor     bgn     end elapsed
## 5 fit.models          4          1           1 342.922 418.836  75.914
## 6 fit.models          4          2           2 418.837      NA      NA

fit.models_2_chunk_df <- 
    myadd_chunk(NULL, "fit.models_2_bgn", label.minor = "setup")

##              label step_major step_minor label_minor     bgn end elapsed
## 1 fit.models_2_bgn          1          0       setup 423.208  NA      NA

plt_models_df <- glb_models_df[, -grep("SD|Upper|Lower", names(glb_models_df))]
for (var in grep("^min.", names(plt_models_df), value=TRUE)) {
    plt_models_df[, sub("min.", "inv.", var)] <- 
        #ifelse(all(is.na(tmp <- plt_models_df[, var])), NA, 1.0 / tmp)
        1.0 / plt_models_df[, var]
    plt_models_df <- plt_models_df[ , -grep(var, names(plt_models_df))]
}
print(plt_models_df)

##                                                              id
## MFO###myMFO_classfr                         MFO###myMFO_classfr
## Random###myrandom_classfr             Random###myrandom_classfr
## Max.cor.Y.rcv.1X1###glmnet           Max.cor.Y.rcv.1X1###glmnet
## Max.cor.Y##rcv#rpart                       Max.cor.Y##rcv#rpart
## Interact.High.cor.Y##rcv#glmnet Interact.High.cor.Y##rcv#glmnet
## Low.cor.X##rcv#glmnet                     Low.cor.X##rcv#glmnet
## All.X##rcv#glmnet                             All.X##rcv#glmnet
## All.X##rcv#glm                                   All.X##rcv#glm
##                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             feats
## MFO###myMFO_classfr                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        .rnorm
## Random###myrandom_classfr                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  .rnorm
## Max.cor.Y.rcv.1X1###glmnet                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               Q109244.fctr,Gender.fctr
## Max.cor.Y##rcv#rpart                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     Q109244.fctr,Gender.fctr
## Interact.High.cor.Y##rcv#glmnet                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            Q109244.fctr,Gender.fctr,Q109244.fctr:Q99480.fctr,Q109244.fctr:Q98078.fctr,Q109244.fctr:Q100689.fctr,Q109244.fctr:Q108855.fctr,Q109244.fctr:Q123621.fctr,Q109244.fctr:Q122771.fctr,Q109244.fctr:Q120472.fctr,Q109244.fctr:Q106272.fctr
## Low.cor.X##rcv#glmnet           Q109244.fctr,Hhold.fctr,Edn.fctr,Q101163.fctr,Q100689.fctr,Q98078.fctr,Q99716.fctr,Q120379.fctr,Q121699.fctr,Q105840.fctr,Q113583.fctr,Q115195.fctr,Q102089.fctr,Q98059.fctr,Q114386.fctr,Q100680.fctr,Q108342.fctr,Q111848.fctr,YOB.Age.fctr,Q118892.fctr,Q102687.fctr,Q115390.fctr,Q119851.fctr,Q114517.fctr,Q120012.fctr,Q109367.fctr,Q114961.fctr,Q121700.fctr,Q124122.fctr,Q111220.fctr,Q113992.fctr,Q121011.fctr,Q106042.fctr,Q116448.fctr,Q116601.fctr,Q104996.fctr,Q102906.fctr,Q113584.fctr,Q108950.fctr,Q102674.fctr,Q103293.fctr,Q112478.fctr,Q114748.fctr,Q107491.fctr,Q100562.fctr,Q108617.fctr,Q100010.fctr,Q115602.fctr,Q116953.fctr,Q115610.fctr,Q106997.fctr,Q120978.fctr,Q112512.fctr,Q108343.fctr,Q96024.fctr,Q106389.fctr,.rnorm,Q108754.fctr,Q98578.fctr,Q101162.fctr,Q115777.fctr,Q99581.fctr,Q124742.fctr,Q116797.fctr,Q112270.fctr,Q118237.fctr,Q119650.fctr,Q111580.fctr,Q123464.fctr,Q117193.fctr,Q99982.fctr,Q108856.fctr,Q118233.fctr,Q102289.fctr,Q116197.fctr,Income.fctr,Q118232.fctr,Q120194.fctr,Q114152.fctr,Q122770.fctr,Q117186.fctr,Q105655.fctr,Q106993.fctr,Q119334.fctr,Q122120.fctr,Q116441.fctr,Q118117.fctr,Q123621.fctr,Q122769.fctr,Q120650.fctr,Q98869.fctr,Q107869.fctr,Q120014.fctr,Q115899.fctr,Q106388.fctr,Q99480.fctr,Q122771.fctr,Q108855.fctr,Q110740.fctr,Q106272.fctr,Q101596.fctr,Q116881.fctr,Q120472.fctr,Q98197.fctr,Q113181.fctr,Q115611.fctr,Gender.fctr,Hhold.fctr:.clusterid.fctr
## All.X##rcv#glmnet               Q109244.fctr,Hhold.fctr,Edn.fctr,Q101163.fctr,Q100689.fctr,Q98078.fctr,Q99716.fctr,Q120379.fctr,Q121699.fctr,Q105840.fctr,Q113583.fctr,Q115195.fctr,Q102089.fctr,Q98059.fctr,Q114386.fctr,Q100680.fctr,Q108342.fctr,Q111848.fctr,YOB.Age.fctr,Q118892.fctr,Q102687.fctr,Q115390.fctr,Q119851.fctr,Q114517.fctr,Q120012.fctr,Q109367.fctr,Q114961.fctr,Q121700.fctr,Q124122.fctr,Q111220.fctr,Q113992.fctr,Q121011.fctr,Q106042.fctr,Q116448.fctr,Q116601.fctr,Q104996.fctr,Q102906.fctr,Q113584.fctr,Q108950.fctr,Q102674.fctr,Q103293.fctr,Q112478.fctr,Q114748.fctr,Q107491.fctr,Q100562.fctr,Q108617.fctr,Q100010.fctr,Q115602.fctr,Q116953.fctr,Q115610.fctr,Q106997.fctr,Q120978.fctr,Q112512.fctr,Q108343.fctr,Q96024.fctr,Q106389.fctr,.rnorm,Q108754.fctr,Q98578.fctr,Q101162.fctr,Q115777.fctr,Q99581.fctr,Q124742.fctr,Q116797.fctr,Q112270.fctr,Q118237.fctr,Q119650.fctr,Q111580.fctr,Q123464.fctr,Q117193.fctr,Q99982.fctr,Q108856.fctr,Q118233.fctr,Q102289.fctr,Q116197.fctr,Income.fctr,Q118232.fctr,Q120194.fctr,Q114152.fctr,Q122770.fctr,Q117186.fctr,Q105655.fctr,Q106993.fctr,Q119334.fctr,Q122120.fctr,Q116441.fctr,Q118117.fctr,Q123621.fctr,Q122769.fctr,Q120650.fctr,Q98869.fctr,Q107869.fctr,Q120014.fctr,Q115899.fctr,Q106388.fctr,Q99480.fctr,Q122771.fctr,Q108855.fctr,Q110740.fctr,Q106272.fctr,Q101596.fctr,Q116881.fctr,Q120472.fctr,Q98197.fctr,Q113181.fctr,Q115611.fctr,Gender.fctr,Hhold.fctr:.clusterid.fctr
## All.X##rcv#glm                  Q109244.fctr,Hhold.fctr,Edn.fctr,Q101163.fctr,Q100689.fctr,Q98078.fctr,Q99716.fctr,Q120379.fctr,Q121699.fctr,Q105840.fctr,Q113583.fctr,Q115195.fctr,Q102089.fctr,Q98059.fctr,Q114386.fctr,Q100680.fctr,Q108342.fctr,Q111848.fctr,YOB.Age.fctr,Q118892.fctr,Q102687.fctr,Q115390.fctr,Q119851.fctr,Q114517.fctr,Q120012.fctr,Q109367.fctr,Q114961.fctr,Q121700.fctr,Q124122.fctr,Q111220.fctr,Q113992.fctr,Q121011.fctr,Q106042.fctr,Q116448.fctr,Q116601.fctr,Q104996.fctr,Q102906.fctr,Q113584.fctr,Q108950.fctr,Q102674.fctr,Q103293.fctr,Q112478.fctr,Q114748.fctr,Q107491.fctr,Q100562.fctr,Q108617.fctr,Q100010.fctr,Q115602.fctr,Q116953.fctr,Q115610.fctr,Q106997.fctr,Q120978.fctr,Q112512.fctr,Q108343.fctr,Q96024.fctr,Q106389.fctr,.rnorm,Q108754.fctr,Q98578.fctr,Q101162.fctr,Q115777.fctr,Q99581.fctr,Q124742.fctr,Q116797.fctr,Q112270.fctr,Q118237.fctr,Q119650.fctr,Q111580.fctr,Q123464.fctr,Q117193.fctr,Q99982.fctr,Q108856.fctr,Q118233.fctr,Q102289.fctr,Q116197.fctr,Income.fctr,Q118232.fctr,Q120194.fctr,Q114152.fctr,Q122770.fctr,Q117186.fctr,Q105655.fctr,Q106993.fctr,Q119334.fctr,Q122120.fctr,Q116441.fctr,Q118117.fctr,Q123621.fctr,Q122769.fctr,Q120650.fctr,Q98869.fctr,Q107869.fctr,Q120014.fctr,Q115899.fctr,Q106388.fctr,Q99480.fctr,Q122771.fctr,Q108855.fctr,Q110740.fctr,Q106272.fctr,Q101596.fctr,Q116881.fctr,Q120472.fctr,Q98197.fctr,Q113181.fctr,Q115611.fctr,Gender.fctr,Hhold.fctr:.clusterid.fctr
##                                 max.nTuningRuns max.AUCpROC.fit
## MFO###myMFO_classfr                           0       0.5000000
## Random###myrandom_classfr                     0       0.4942483
## Max.cor.Y.rcv.1X1###glmnet                    0       0.5971118
## Max.cor.Y##rcv#rpart                          5       0.5971118
## Interact.High.cor.Y##rcv#glmnet              25       0.6184781
## Low.cor.X##rcv#glmnet                        25       0.6273089
## All.X##rcv#glmnet                            25       0.6273089
## All.X##rcv#glm                                1       0.6709241
##                                 max.Sens.fit max.Spec.fit max.AUCROCR.fit
## MFO###myMFO_classfr                0.0000000    1.0000000       0.5000000
## Random###myrandom_classfr          0.4619799    0.5265168       0.5073101
## Max.cor.Y.rcv.1X1###glmnet         0.5480631    0.6461604       0.3580613
## Max.cor.Y##rcv#rpart               0.5480631    0.6461604       0.3676308
## Interact.High.cor.Y##rcv#glmnet    0.5958871    0.6410692       0.3319465
## Low.cor.X##rcv#glmnet              0.5155428    0.7390751       0.3048695
## All.X##rcv#glmnet                  0.5155428    0.7390751       0.3048695
## All.X##rcv#glm                     0.6456241    0.6962240       0.2603537
##                                 opt.prob.threshold.fit max.f.score.fit
## MFO###myMFO_classfr                               0.50       0.6395473
## Random###myrandom_classfr                         0.55       0.6395473
## Max.cor.Y.rcv.1X1###glmnet                        0.60       0.6720662
## Max.cor.Y##rcv#rpart                              0.55       0.6720662
## Interact.High.cor.Y##rcv#glmnet                   0.65       0.6727114
## Low.cor.X##rcv#glmnet                             0.60       0.6756231
## All.X##rcv#glmnet                                 0.60       0.6756231
## All.X##rcv#glm                                    0.65       0.6933837
##                                 max.Accuracy.fit max.Kappa.fit
## MFO###myMFO_classfr                    0.4700989     0.0000000
## Random###myrandom_classfr              0.4700989     0.0000000
## Max.cor.Y.rcv.1X1###glmnet             0.5721673     0.1772539
## Max.cor.Y##rcv#rpart                   0.6000450     0.1947896
## Interact.High.cor.Y##rcv#glmnet        0.6058167     0.2088694
## Low.cor.X##rcv#glmnet                  0.6247001     0.2399024
## All.X##rcv#glmnet                      0.6247001     0.2399024
## All.X##rcv#glm                         0.6013205     0.1993351
##                                 max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB
## MFO###myMFO_classfr                   0.5000000    0.0000000    1.0000000
## Random###myrandom_classfr             0.5235690    0.5000000    0.5471380
## Max.cor.Y.rcv.1X1###glmnet            0.5896897    0.5228137    0.6565657
## Max.cor.Y##rcv#rpart                  0.5896897    0.5228137    0.6565657
## Interact.High.cor.Y##rcv#glmnet       0.6031353    0.5665399    0.6397306
## Low.cor.X##rcv#glmnet                 0.6237374    0.5000000    0.7474747
## All.X##rcv#glmnet                     0.6237374    0.5000000    0.7474747
## All.X##rcv#glm                        0.5934920    0.5304183    0.6565657
##                                 max.AUCROCR.OOB opt.prob.threshold.OOB
## MFO###myMFO_classfr                   0.5000000                   0.50
## Random###myrandom_classfr             0.5191202                   0.55
## Max.cor.Y.rcv.1X1###glmnet            0.3658672                   0.60
## Max.cor.Y##rcv#rpart                  0.3774772                   0.55
## Interact.High.cor.Y##rcv#glmnet       0.3571392                   0.65
## Low.cor.X##rcv#glmnet                 0.3184875                   0.70
## All.X##rcv#glmnet                     0.3184875                   0.70
## All.X##rcv#glm                        0.3391328                   0.75
##                                 max.f.score.OOB max.Accuracy.OOB
## MFO###myMFO_classfr                   0.6391252        0.4696429
## Random###myrandom_classfr             0.6391252        0.4696429
## Max.cor.Y.rcv.1X1###glmnet            0.6643789        0.5633929
## Max.cor.Y##rcv#rpart                  0.6643789        0.5633929
## Interact.High.cor.Y##rcv#glmnet       0.6680556        0.5732143
## Low.cor.X##rcv#glmnet                 0.6644250        0.5544643
## All.X##rcv#glmnet                     0.6644250        0.5544643
## All.X##rcv#glm                        0.6671261        0.5687500
##                                 max.Kappa.OOB inv.elapsedtime.everything
## MFO###myMFO_classfr                 0.0000000                 1.59235669
## Random###myrandom_classfr           0.0000000                 3.42465753
## Max.cor.Y.rcv.1X1###glmnet          0.1605510                 1.12107623
## Max.cor.Y##rcv#rpart                0.1605510                 0.45641260
## Interact.High.cor.Y##rcv#glmnet     0.1779779                 0.15130882
## Low.cor.X##rcv#glmnet               0.1460545                 0.04108632
## All.X##rcv#glmnet                   0.1460545                 0.04150411
## All.X##rcv#glm                      0.1703322                 0.05301103
##                                 inv.elapsedtime.final
## MFO###myMFO_classfr                       250.0000000
## Random###myrandom_classfr                 333.3333333
## Max.cor.Y.rcv.1X1###glmnet                 15.6250000
## Max.cor.Y##rcv#rpart                       40.0000000
## Interact.High.cor.Y##rcv#glmnet             2.7777778
## Low.cor.X##rcv#glmnet                       0.4716981
## All.X##rcv#glmnet                           0.4750594
## All.X##rcv#glm                              0.5221932

# print(myplot_radar(radar_inp_df=plt_models_df))
# print(myplot_radar(radar_inp_df=subset(plt_models_df, 
#         !(mdl_id %in% grep("random|MFO", plt_models_df$id, value=TRUE)))))

# Compute CI for <metric>SD
glb_models_df <- mutate(glb_models_df, 
                max.df = ifelse(max.nTuningRuns > 1, max.nTuningRuns - 1, NA),
                min.sd2ci.scaler = ifelse(is.na(max.df), NA, qt(0.975, max.df)))
for (var in grep("SD", names(glb_models_df), value=TRUE)) {
    # Does CI alredy exist ?
    var_components <- unlist(strsplit(var, "SD"))
    varActul <- paste0(var_components[1],          var_components[2])
    varUpper <- paste0(var_components[1], "Upper", var_components[2])
    varLower <- paste0(var_components[1], "Lower", var_components[2])
    if (varUpper %in% names(glb_models_df)) {
        warning(varUpper, " already exists in glb_models_df")
        # Assuming Lower also exists
        next
    }    
    print(sprintf("var:%s", var))
    # CI is dependent on sample size in t distribution; df=n-1
    glb_models_df[, varUpper] <- glb_models_df[, varActul] + 
        glb_models_df[, "min.sd2ci.scaler"] * glb_models_df[, var]
    glb_models_df[, varLower] <- glb_models_df[, varActul] - 
        glb_models_df[, "min.sd2ci.scaler"] * glb_models_df[, var]
}

## Warning: max.AccuracyUpper.fit already exists in glb_models_df

## [1] "var:max.KappaSD.fit"

# Plot metrics with CI
plt_models_df <- glb_models_df[, "id", FALSE]
pltCI_models_df <- glb_models_df[, "id", FALSE]
for (var in grep("Upper", names(glb_models_df), value=TRUE)) {
    var_components <- unlist(strsplit(var, "Upper"))
    col_name <- unlist(paste(var_components, collapse=""))
    plt_models_df[, col_name] <- glb_models_df[, col_name]
    for (name in paste0(var_components[1], c("Upper", "Lower"), var_components[2]))
        pltCI_models_df[, name] <- glb_models_df[, name]
}

build_statsCI_data <- function(plt_models_df) {
    mltd_models_df <- melt(plt_models_df, id.vars="id")
    mltd_models_df$data <- sapply(1:nrow(mltd_models_df), 
        function(row_ix) tail(unlist(strsplit(as.character(
            mltd_models_df[row_ix, "variable"]), "[.]")), 1))
    mltd_models_df$label <- sapply(1:nrow(mltd_models_df), 
        function(row_ix) head(unlist(strsplit(as.character(
            mltd_models_df[row_ix, "variable"]), 
            paste0(".", mltd_models_df[row_ix, "data"]))), 1))
    #print(mltd_models_df)
    
    return(mltd_models_df)
}
mltd_models_df <- build_statsCI_data(plt_models_df)

mltdCI_models_df <- melt(pltCI_models_df, id.vars="id")
for (row_ix in 1:nrow(mltdCI_models_df)) {
    for (type in c("Upper", "Lower")) {
        if (length(var_components <- unlist(strsplit(
                as.character(mltdCI_models_df[row_ix, "variable"]), type))) > 1) {
            #print(sprintf("row_ix:%d; type:%s; ", row_ix, type))
            mltdCI_models_df[row_ix, "label"] <- var_components[1]
            mltdCI_models_df[row_ix, "data"] <- 
                unlist(strsplit(var_components[2], "[.]"))[2]
            mltdCI_models_df[row_ix, "type"] <- type
            break
        }
    }    
}
wideCI_models_df <- reshape(subset(mltdCI_models_df, select=-variable), 
                            timevar="type", 
        idvar=setdiff(names(mltdCI_models_df), c("type", "value", "variable")), 
                            direction="wide")
#print(wideCI_models_df)
mrgdCI_models_df <- merge(wideCI_models_df, mltd_models_df, all.x=TRUE)
#print(mrgdCI_models_df)

# Merge stats back in if CIs don't exist
goback_vars <- c()
for (var in unique(mltd_models_df$label)) {
    for (type in unique(mltd_models_df$data)) {
        var_type <- paste0(var, ".", type)
        # if this data is already present, next
        if (var_type %in% unique(paste(mltd_models_df$label, mltd_models_df$data,
                                       sep=".")))
            next
        #print(sprintf("var_type:%s", var_type))
        goback_vars <- c(goback_vars, var_type)
    }
}

if (length(goback_vars) > 0) {
    mltd_goback_df <- build_statsCI_data(glb_models_df[, c("id", goback_vars)])
    mltd_models_df <- rbind(mltd_models_df, mltd_goback_df)
}

# mltd_models_df <- merge(mltd_models_df, glb_models_df[, c("id", "model_method")], 
#                         all.x=TRUE)

png(paste0(glbOut$pfx, "models_bar.png"), width=480*3, height=480*2)
#print(gp <- myplot_bar(mltd_models_df, "id", "value", colorcol_name="model_method") + 
print(gp <- myplot_bar(df=mltd_models_df, xcol_name="id", ycol_names="value") + 
        geom_errorbar(data=mrgdCI_models_df, 
            mapping=aes(x=mdl_id, ymax=value.Upper, ymin=value.Lower), width=0.5) + 
          facet_grid(label ~ data, scales="free") + 
          theme(axis.text.x = element_text(angle = 90,vjust = 0.5)))

## Warning: Removed 4 rows containing missing values (geom_errorbar).

dev.off()

## quartz_off_screen 
##                 2

print(gp)

## Warning: Removed 4 rows containing missing values (geom_errorbar).

dsp_models_cols <- c("id", 
                    glbMdlMetricsEval[glbMdlMetricsEval %in% names(glb_models_df)],
                    grep("opt.", names(glb_models_df), fixed = TRUE, value = TRUE)) 
# if (glb_is_classification && glb_is_binomial) 
#     dsp_models_cols <- c(dsp_models_cols, "opt.prob.threshold.OOB")
print(dsp_models_df <- orderBy(get_model_sel_frmla(), glb_models_df)[, dsp_models_cols])

##                                id max.Accuracy.OOB max.AUCROCR.OOB
## 5 Interact.High.cor.Y##rcv#glmnet        0.5732143       0.3571392
## 8                  All.X##rcv#glm        0.5687500       0.3391328
## 4            Max.cor.Y##rcv#rpart        0.5633929       0.3774772
## 3      Max.cor.Y.rcv.1X1###glmnet        0.5633929       0.3658672
## 6           Low.cor.X##rcv#glmnet        0.5544643       0.3184875
## 7               All.X##rcv#glmnet        0.5544643       0.3184875
## 2       Random###myrandom_classfr        0.4696429       0.5191202
## 1             MFO###myMFO_classfr        0.4696429       0.5000000
##   max.AUCpROC.OOB max.Accuracy.fit opt.prob.threshold.fit
## 5       0.6031353        0.6058167                   0.65
## 8       0.5934920        0.6013205                   0.65
## 4       0.5896897        0.6000450                   0.55
## 3       0.5896897        0.5721673                   0.60
## 6       0.6237374        0.6247001                   0.60
## 7       0.6237374        0.6247001                   0.60
## 2       0.5235690        0.4700989                   0.55
## 1       0.5000000        0.4700989                   0.50
##   opt.prob.threshold.OOB
## 5                   0.65
## 8                   0.75
## 4                   0.55
## 3                   0.60
## 6                   0.70
## 7                   0.70
## 2                   0.55
## 1                   0.50

# print(myplot_radar(radar_inp_df = dsp_models_df))
print("Metrics used for model selection:"); print(get_model_sel_frmla())

## [1] "Metrics used for model selection:"

## ~-max.Accuracy.OOB - max.AUCROCR.OOB - max.AUCpROC.OOB - max.Accuracy.fit - 
##     opt.prob.threshold.OOB
## <environment: 0x7fa03f5490d0>

print(sprintf("Best model id: %s", dsp_models_df[1, "id"]))

## [1] "Best model id: Interact.High.cor.Y##rcv#glmnet"

glb_get_predictions <- function(df, mdl_id, rsp_var, prob_threshold_def=NULL, verbose=FALSE) {
    mdl <- glb_models_lst[[mdl_id]]
    
    clmnNames <- mygetPredictIds(rsp_var, mdl_id)
    predct_var_name <- clmnNames$value        
    predct_prob_var_name <- clmnNames$prob
    predct_accurate_var_name <- clmnNames$is.acc
    predct_error_var_name <- clmnNames$err
    predct_erabs_var_name <- clmnNames$err.abs

    if (glb_is_regression) {
        df[, predct_var_name] <- predict(mdl, newdata=df, type="raw")
        if (verbose) print(myplot_scatter(df, glb_rsp_var, predct_var_name) + 
                  facet_wrap(reformulate(glbFeatsCategory), scales = "free") + 
                  stat_smooth(method="glm"))

        df[, predct_error_var_name] <- df[, predct_var_name] - df[, glb_rsp_var]
        if (verbose) print(myplot_scatter(df, predct_var_name, predct_error_var_name) + 
                  #facet_wrap(reformulate(glbFeatsCategory), scales = "free") + 
                  stat_smooth(method="auto"))
        if (verbose) print(myplot_scatter(df, glb_rsp_var, predct_error_var_name) + 
                  #facet_wrap(reformulate(glbFeatsCategory), scales = "free") + 
                  stat_smooth(method="glm"))
        
        df[, predct_erabs_var_name] <- abs(df[, predct_error_var_name])
        if (verbose) print(head(orderBy(reformulate(c("-", predct_erabs_var_name)), df)))
        
        df[, predct_accurate_var_name] <- (df[, glb_rsp_var] == df[, predct_var_name])
    }

    if (glb_is_classification && glb_is_binomial) {
        prob_threshold <- glb_models_df[glb_models_df$id == mdl_id, 
                                        "opt.prob.threshold.OOB"]
        if (is.null(prob_threshold) || is.na(prob_threshold)) {
            warning("Using default probability threshold: ", prob_threshold_def)
            if (is.null(prob_threshold <- prob_threshold_def))
                stop("Default probability threshold is NULL")
        }
        
        df[, predct_prob_var_name] <- predict(mdl, newdata = df, type = "prob")[, 2]
        df[, predct_var_name] <- 
                factor(levels(df[, glb_rsp_var])[
                    (df[, predct_prob_var_name] >=
                        prob_threshold) * 1 + 1], levels(df[, glb_rsp_var]))
    
#         if (verbose) print(myplot_scatter(df, glb_rsp_var, predct_var_name) + 
#                   facet_wrap(reformulate(glbFeatsCategory), scales = "free") + 
#                   stat_smooth(method="glm"))

        df[, predct_error_var_name] <- df[, predct_var_name] != df[, glb_rsp_var]
#         if (verbose) print(myplot_scatter(df, predct_var_name, predct_error_var_name) + 
#                   #facet_wrap(reformulate(glbFeatsCategory), scales = "free") + 
#                   stat_smooth(method="auto"))
#         if (verbose) print(myplot_scatter(df, glb_rsp_var, predct_error_var_name) + 
#                   #facet_wrap(reformulate(glbFeatsCategory), scales = "free") + 
#                   stat_smooth(method="glm"))
        
        # if prediction is a TP (true +ve), measure distance from 1.0
        tp <- which((df[, predct_var_name] == df[, glb_rsp_var]) &
                    (df[, predct_var_name] == levels(df[, glb_rsp_var])[2]))
        df[tp, predct_erabs_var_name] <- abs(1 - df[tp, predct_prob_var_name])
        #rowIx <- which.max(df[tp, predct_erabs_var_name]); df[tp, c(glbFeatsId, glb_rsp_var, predct_var_name, predct_prob_var_name, predct_erabs_var_name)][rowIx, ]
        
        # if prediction is a TN (true -ve), measure distance from 0.0
        tn <- which((df[, predct_var_name] == df[, glb_rsp_var]) &
                    (df[, predct_var_name] == levels(df[, glb_rsp_var])[1]))
        df[tn, predct_erabs_var_name] <- abs(0 - df[tn, predct_prob_var_name])
        #rowIx <- which.max(df[tn, predct_erabs_var_name]); df[tn, c(glbFeatsId, glb_rsp_var, predct_var_name, predct_prob_var_name, predct_erabs_var_name)][rowIx, ]
        
        # if prediction is a FP (flse +ve), measure distance from 0.0
        fp <- which((df[, predct_var_name] != df[, glb_rsp_var]) &
                    (df[, predct_var_name] == levels(df[, glb_rsp_var])[2]))
        df[fp, predct_erabs_var_name] <- abs(0 - df[fp, predct_prob_var_name])
        #rowIx <- which.max(df[fp, predct_erabs_var_name]); df[fp, c(glbFeatsId, glb_rsp_var, predct_var_name, predct_prob_var_name, predct_erabs_var_name)][rowIx, ]
        
        # if prediction is a FN (flse -ve), measure distance from 1.0
        fn <- which((df[, predct_var_name] != df[, glb_rsp_var]) &
                    (df[, predct_var_name] == levels(df[, glb_rsp_var])[1]))
        df[fn, predct_erabs_var_name] <- abs(1 - df[fn, predct_prob_var_name])
        #rowIx <- which.max(df[fn, predct_erabs_var_name]); df[fn, c(glbFeatsId, glb_rsp_var, predct_var_name, predct_prob_var_name, predct_erabs_var_name)][rowIx, ]

        
        if (verbose) print(head(orderBy(reformulate(c("-", predct_erabs_var_name)), df)))
        
        df[, predct_accurate_var_name] <- (df[, glb_rsp_var] == df[, predct_var_name])
    }    
    
    if (glb_is_classification && !glb_is_binomial) {
        df[, predct_var_name] <- predict(mdl, newdata = df, type = "raw")
        probCls <- predict(mdl, newdata = df, type = "prob")        
        df[, predct_prob_var_name] <- NA
        for (cls in names(probCls)) {
            mask <- (df[, predct_var_name] == cls)
            df[mask, predct_prob_var_name] <- probCls[mask, cls]
        }    
        if (verbose) print(myplot_histogram(df, predct_prob_var_name, 
                                            fill_col_name = predct_var_name))
        if (verbose) print(myplot_histogram(df, predct_prob_var_name, 
                                            facet_frmla = paste0("~", glb_rsp_var)))
        
        df[, predct_error_var_name] <- df[, predct_var_name] != df[, glb_rsp_var]
        
        # if prediction is erroneous, measure predicted class prob from actual class prob
        df[, predct_erabs_var_name] <- 0
        for (cls in names(probCls)) {
            mask <- (df[, glb_rsp_var] == cls) & (df[, predct_error_var_name])
            df[mask, predct_erabs_var_name] <- probCls[mask, cls]
        }    

        df[, predct_accurate_var_name] <- (df[, glb_rsp_var] == df[, predct_var_name])        
    }

    return(df)
}    

#stop(here"); glb2Sav(); glbObsAll <- savObsAll; glbObsTrn <- savObsTrn; glbObsFit <- savObsFit; glbObsOOB <- savObsOOB; sav_models_df <- glb_models_df; glb_models_df <- sav_models_df; glb_featsimp_df <- sav_featsimp_df    

myget_category_stats <- function(obs_df, mdl_id, label) {
    require(dplyr)
    require(lazyeval)
    
    predct_var_name <- mygetPredictIds(glb_rsp_var, mdl_id)$value        
    predct_error_var_name <- mygetPredictIds(glb_rsp_var, mdl_id)$err.abs
    
    if (!predct_var_name %in% names(obs_df))
        obs_df <- glb_get_predictions(obs_df, mdl_id, glb_rsp_var)
    
    tmp_obs_df <- obs_df[, c(glbFeatsCategory, glb_rsp_var, 
                             predct_var_name, predct_error_var_name)]
#     tmp_obs_df <- obs_df %>%
#         dplyr::select_(glbFeatsCategory, glb_rsp_var, predct_var_name, predct_error_var_name) 
    #dplyr::rename(startprice.log10.predict.RFE.X.glmnet.err=error_abs_OOB)
    names(tmp_obs_df)[length(names(tmp_obs_df))] <- paste0("err.abs.", label)
    
    ret_ctgry_df <- tmp_obs_df %>%
        dplyr::group_by_(glbFeatsCategory) %>%
        dplyr::summarise_(#interp(~sum(abs(var)), var=as.name(glb_rsp_var)), 
            interp(~sum(var), var=as.name(paste0("err.abs.", label))), 
            interp(~mean(var), var=as.name(paste0("err.abs.", label))),
            interp(~n()))
    names(ret_ctgry_df) <- c(glbFeatsCategory, 
                             #paste0(glb_rsp_var, ".abs.", label, ".sum"),
                             paste0("err.abs.", label, ".sum"),                             
                             paste0("err.abs.", label, ".mean"), 
                             paste0(".n.", label))
    ret_ctgry_df <- dplyr::ungroup(ret_ctgry_df)
    #colSums(ret_ctgry_df[, -grep(glbFeatsCategory, names(ret_ctgry_df))])
    
    return(ret_ctgry_df)    
}
#print(colSums((ctgry_df <- myget_category_stats(obs_df=glbObsFit, mdl_id="", label="fit"))[, -grep(glbFeatsCategory, names(ctgry_df))]))

if (!is.null(glb_mdl_ensemble)) {
    fit.models_2_chunk_df <- myadd_chunk(fit.models_2_chunk_df, 
                            paste0("fit.models_2_", mdl_id_pfx), major.inc = TRUE, 
                                                label.minor = "ensemble")
    
    mdl_id_pfx <- "Ensemble"

    if (#(glb_is_regression) | 
        ((glb_is_classification) & (!glb_is_binomial)))
        stop("Ensemble models not implemented yet for multinomial classification")
    
    mygetEnsembleAutoMdlIds <- function() {
        tmp_models_df <- orderBy(get_model_sel_frmla(), glb_models_df)
        row.names(tmp_models_df) <- tmp_models_df$id
        mdl_threshold_pos <- 
            min(which(grepl("MFO|Random|Baseline", tmp_models_df$id))) - 1
        mdlIds <- tmp_models_df$id[1:mdl_threshold_pos]
        return(mdlIds[!grepl("Ensemble", mdlIds)])
    }
    
    if (glb_mdl_ensemble == "auto") {
        glb_mdl_ensemble <- mygetEnsembleAutoMdlIds()
        mdl_id_pfx <- paste0(mdl_id_pfx, ".auto")        
    } else if (grepl("^%<d-%", glb_mdl_ensemble)) {
        glb_mdl_ensemble <- eval(parse(text =
                        str_trim(unlist(strsplit(glb_mdl_ensemble, "%<d-%"))[2])))
    }
    
    for (mdl_id in glb_mdl_ensemble) {
        if (!(mdl_id %in% names(glb_models_lst))) {
            warning("Model ", mdl_id, " in glb_model_ensemble not found !")
            next
        }
        glbObsFit <- glb_get_predictions(df = glbObsFit, mdl_id, glb_rsp_var)
        glbObsOOB <- glb_get_predictions(df = glbObsOOB, mdl_id, glb_rsp_var)
    }
    
#mdl_id_pfx <- "Ensemble.RFE"; mdlId <- paste0(mdl_id_pfx, ".glmnet")
#glb_mdl_ensemble <- gsub(mygetPredictIds$value, "", grep("RFE\\.X\\.(?!Interact)", row.names(glb_featsimp_df), perl = TRUE, value = TRUE), fixed = TRUE)
#varImp(glb_models_lst[[mdlId]])
    
#cor_df <- data.frame(cor=cor(glbObsFit[, glb_rsp_var], glbObsFit[, paste(mygetPredictIds$value, glb_mdl_ensemble)], use="pairwise.complete.obs"))
#glbObsFit <- glb_get_predictions(df=glbObsFit, "Ensemble.glmnet", glb_rsp_var);print(colSums((ctgry_df <- myget_category_stats(obs_df=glbObsFit, mdl_id="Ensemble.glmnet", label="fit"))[, -grep(glbFeatsCategory, names(ctgry_df))]))
    
    ### bid0_sp
    #  Better than MFO; models.n=28; min.RMSE.fit=0.0521233; err.abs.fit.sum=7.3631895
    #  old: Top x from auto; models.n= 5; min.RMSE.fit=0.06311047; err.abs.fit.sum=9.5937080
    #  RFE only ;       models.n=16; min.RMSE.fit=0.05148588; err.abs.fit.sum=7.2875091
    #  RFE subset only ;models.n= 5; min.RMSE.fit=0.06040702; err.abs.fit.sum=9.059088
    #  RFE subset only ;models.n= 9; min.RMSE.fit=0.05933167; err.abs.fit.sum=8.7421288
    #  RFE subset only ;models.n=15; min.RMSE.fit=0.0584607; err.abs.fit.sum=8.5902066
    #  RFE subset only ;models.n=17; min.RMSE.fit=0.05496899; err.abs.fit.sum=8.0170431
    #  RFE subset only ;models.n=18; min.RMSE.fit=0.05441577; err.abs.fit.sum=7.837223
    #  RFE subset only ;models.n=16; min.RMSE.fit=0.05441577; err.abs.fit.sum=7.837223
    ### bid0_sp
    ### bid1_sp
    # "auto"; err.abs.fit.sum=76.699774; min.RMSE.fit=0.2186429
    # "RFE.X.*"; err.abs.fit.sum=; min.RMSE.fit=0.221114
    ### bid1_sp

    indepVar <- paste(mygetPredictIds(glb_rsp_var)$value, glb_mdl_ensemble, sep = "")
    if (glb_is_classification)
        indepVar <- paste(indepVar, ".prob", sep = "")
    # Some models in glb_mdl_ensemble might not be fitted e.g. RFE.X.Interact
    indepVar <- intersect(indepVar, names(glbObsFit))
    
#     indepVar <- grep(mygetPredictIds(glb_rsp_var)$value, names(glbObsFit), fixed=TRUE, value=TRUE)
#     if (glb_is_regression)
#         indepVar <- indepVar[!grepl("(err\\.abs|accurate)$", indepVar)]
#     if (glb_is_classification && glb_is_binomial)
#         indepVar <- grep("prob$", indepVar, value=TRUE) else
#         indepVar <- indepVar[!grepl("err$", indepVar)]

    #rfe_fit_ens_results <- myrun_rfe(glbObsFit, indepVar)
    
    for (method in c("glm", "glmnet")) {
        for (trainControlMethod in 
             c("boot", "boot632", "cv", "repeatedcv"
               #, "LOOCV" # tuneLength * nrow(fitDF)
               , "LGOCV", "adaptive_cv"
               #, "adaptive_boot"  #error: adaptive$min should be less than 3 
               #, "adaptive_LGOCV" #error: adaptive$min should be less than 3 
               )) {
            #sav_models_df <- glb_models_df; all.equal(sav_models_df, glb_models_df)
            #glb_models_df <- sav_models_df; print(glb_models_df$id)
                
            if ((method == "glm") && (trainControlMethod != "repeatedcv"))
                # glm used only to identify outliers
                next
            
            ret_lst <- myfit_mdl(
                mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
                    id.prefix = paste0(mdl_id_pfx, ".", trainControlMethod), 
                    type = glb_model_type, tune.df = NULL,
                    trainControl.method = trainControlMethod,
                    trainControl.number = glb_rcv_n_folds,
                    trainControl.repeats = glb_rcv_n_repeats,
                    trainControl.classProbs = glb_is_classification,
                    trainControl.summaryFunction = glbMdlMetricSummaryFn,
                    train.metric = glbMdlMetricSummary, 
                    train.maximize = glbMdlMetricMaximize,    
                    train.method = method)),
                indepVar = indepVar, rsp_var = glb_rsp_var, 
                fit_df = glbObsFit, OOB_df = glbObsOOB)
        }
    }
    dsp_models_df <- get_dsp_models_df()
}

if (is.null(glbMdlSelId)) 
    glbMdlSelId <- dsp_models_df[1, "id"] else 
    print(sprintf("User specified selection: %s", glbMdlSelId))   

## [1] "User specified selection: All.X##rcv#glmnet"

myprint_mdl(glb_sel_mdl <- glb_models_lst[[glbMdlSelId]])

##             Length Class      Mode     
## a0             78  -none-     numeric  
## beta        19734  dgCMatrix  S4       
## df             78  -none-     numeric  
## dim             2  -none-     numeric  
## lambda         78  -none-     numeric  
## dev.ratio      78  -none-     numeric  
## nulldev         1  -none-     numeric  
## npasses         1  -none-     numeric  
## jerr            1  -none-     numeric  
## offset          1  -none-     logical  
## classnames      2  -none-     character
## call            5  -none-     call     
## nobs            1  -none-     numeric  
## lambdaOpt       1  -none-     numeric  
## xNames        253  -none-     character
## problemType     1  -none-     character
## tuneValue       2  data.frame list     
## obsLevels       2  -none-     character
## [1] "min lambda > lambdaOpt:"
##       (Intercept)      Gender.fctrM     Hhold.fctrMKy     Hhold.fctrPKn 
##      0.1759341202     -0.0577396529     -0.0655451865      0.3282606962 
##   Q101163.fctrDad   Q101163.fctrMom    Q109244.fctrNo   Q109244.fctrYes 
##     -0.0462347606      0.0361283260     -0.3170070971      0.7976156342 
##    Q110740.fctrPC    Q113181.fctrNo   Q113181.fctrYes    Q115611.fctrNo 
##     -0.0120813108      0.0348921948     -0.0798654082      0.0770473552 
##   Q115611.fctrYes Q116881.fctrHappy Q116881.fctrRight    Q118232.fctrId 
##     -0.3105852699      0.0087382029     -0.0814535814      0.0180660706 
##    Q119851.fctrNo    Q120379.fctrNo   Q120379.fctrYes     Q98197.fctrNo 
##     -0.0297897358     -0.0006993182      0.0272478036      0.1639383807 
##    Q98197.fctrYes     Q98869.fctrNo     Q99480.fctrNo 
##     -0.0341915807      0.1609634790      0.0576623212 
## [1] "max lambda < lambdaOpt:"
##       (Intercept)      Gender.fctrM     Hhold.fctrMKy     Hhold.fctrPKn 
##       0.180590790      -0.063356510      -0.071963008       0.358397790 
##     Income.fctr.Q   Q101163.fctrDad   Q101163.fctrMom    Q109244.fctrNo 
##      -0.003003778      -0.055383160       0.041352778      -0.326547933 
##   Q109244.fctrYes    Q110740.fctrPC    Q113181.fctrNo   Q113181.fctrYes 
##       0.811987955      -0.023695743       0.041178168      -0.082431818 
##    Q115611.fctrNo   Q115611.fctrYes    Q115899.fctrCs Q116881.fctrHappy 
##       0.080608650      -0.318215302       0.001114558       0.019171363 
## Q116881.fctrRight    Q118232.fctrId    Q119851.fctrNo    Q120379.fctrNo 
##      -0.090653797       0.032794176      -0.043521175      -0.004314002 
##   Q120379.fctrYes    Q121699.fctrNo    Q122771.fctrPt     Q98197.fctrNo 
##       0.038379682      -0.010439127      -0.000907088       0.171596860 
##    Q98197.fctrYes     Q98869.fctrNo     Q99480.fctrNo 
##      -0.034226771       0.174722108       0.068918234

## [1] TRUE

# From here to save(), this should all be in one function
#   these are executed in the same seq twice more:
#       fit.data.training & predict.data.new chunks
print(sprintf("%s fit prediction diagnostics:", glbMdlSelId))

## [1] "All.X##rcv#glmnet fit prediction diagnostics:"

glbObsFit <- glb_get_predictions(df = glbObsFit, mdl_id = glbMdlSelId, 
                                 rsp_var = glb_rsp_var)
print(sprintf("%s OOB prediction diagnostics:", glbMdlSelId))

## [1] "All.X##rcv#glmnet OOB prediction diagnostics:"

glbObsOOB <- glb_get_predictions(df = glbObsOOB, mdl_id = glbMdlSelId, 
                                     rsp_var = glb_rsp_var)

print(glb_featsimp_df <- myget_feats_importance(mdl = glb_sel_mdl, featsimp_df = NULL))

##                                All.X..rcv.glmnet.imp          imp
## Q109244.fctrYes                         100.00000000 100.00000000
## Hhold.fctrPKn                            41.56642724  41.56642724
## Q109244.fctrNo                           39.80933610  39.80933610
## Q115611.fctrYes                          38.97373582  38.97373582
## Q98197.fctrNo                            20.63341630  20.63341630
## Q98869.fctrNo                            20.36490400  20.36490400
## Q116881.fctrRight                        10.34339619  10.34339619
## Q113181.fctrYes                          10.03215580  10.03215580
## Q115611.fctrNo                            9.69659664   9.69659664
## Hhold.fctrMKy                             8.30653632   8.30653632
## Q99480.fctrNo                             7.40277198   7.40277198
## Gender.fctrM                              7.31671883   7.31671883
## Q101163.fctrDad                           5.93777650   5.93777650
## Q101163.fctrMom                           4.60717672   4.60717672
## Q113181.fctrNo                            4.47059621   4.47059621
## Q98197.fctrYes                            4.27686282   4.27686282
## Q119851.fctrNo                            3.95883221   3.95883221
## Q120379.fctrYes                           3.59678995   3.59678995
## Q118232.fctrId                            2.50949804   2.50949804
## Q110740.fctrPC                            1.70814173   1.70814173
## Q116881.fctrHappy                         1.26997411   1.26997411
## Q121699.fctrNo                            0.17720767   0.17720767
## Q120379.fctrNo                            0.14882265   0.14882265
## Income.fctr.Q                             0.05099013   0.05099013
## Q115899.fctrCs                            0.01891999   0.01891999
## Q122771.fctrPt                            0.01539812   0.01539812
## .rnorm                                    0.00000000   0.00000000
## Edn.fctr.L                                0.00000000   0.00000000
## Edn.fctr.Q                                0.00000000   0.00000000
## Edn.fctr.C                                0.00000000   0.00000000
## Edn.fctr^4                                0.00000000   0.00000000
## Edn.fctr^5                                0.00000000   0.00000000
## Edn.fctr^6                                0.00000000   0.00000000
## Edn.fctr^7                                0.00000000   0.00000000
## Gender.fctrF                              0.00000000   0.00000000
## Hhold.fctrMKn                             0.00000000   0.00000000
## Hhold.fctrPKy                             0.00000000   0.00000000
## Hhold.fctrSKn                             0.00000000   0.00000000
## Hhold.fctrSKy                             0.00000000   0.00000000
## Income.fctr.L                             0.00000000   0.00000000
## Income.fctr.C                             0.00000000   0.00000000
## Income.fctr^4                             0.00000000   0.00000000
## Income.fctr^5                             0.00000000   0.00000000
## Income.fctr^6                             0.00000000   0.00000000
## Q100010.fctrNo                            0.00000000   0.00000000
## Q100010.fctrYes                           0.00000000   0.00000000
## Q100562.fctrNo                            0.00000000   0.00000000
## Q100562.fctrYes                           0.00000000   0.00000000
## Q100680.fctrNo                            0.00000000   0.00000000
## Q100680.fctrYes                           0.00000000   0.00000000
## Q100689.fctrNo                            0.00000000   0.00000000
## Q100689.fctrYes                           0.00000000   0.00000000
## Q101162.fctrOptimist                      0.00000000   0.00000000
## Q101162.fctrPessimist                     0.00000000   0.00000000
## Q101596.fctrNo                            0.00000000   0.00000000
## Q101596.fctrYes                           0.00000000   0.00000000
## Q102089.fctrOwn                           0.00000000   0.00000000
## Q102089.fctrRent                          0.00000000   0.00000000
## Q102289.fctrNo                            0.00000000   0.00000000
## Q102289.fctrYes                           0.00000000   0.00000000
## Q102674.fctrNo                            0.00000000   0.00000000
## Q102674.fctrYes                           0.00000000   0.00000000
## Q102687.fctrNo                            0.00000000   0.00000000
## Q102687.fctrYes                           0.00000000   0.00000000
## Q102906.fctrNo                            0.00000000   0.00000000
## Q102906.fctrYes                           0.00000000   0.00000000
## Q103293.fctrNo                            0.00000000   0.00000000
## Q103293.fctrYes                           0.00000000   0.00000000
## Q104996.fctrNo                            0.00000000   0.00000000
## Q104996.fctrYes                           0.00000000   0.00000000
## Q105655.fctrNo                            0.00000000   0.00000000
## Q105655.fctrYes                           0.00000000   0.00000000
## Q105840.fctrNo                            0.00000000   0.00000000
## Q105840.fctrYes                           0.00000000   0.00000000
## Q106042.fctrNo                            0.00000000   0.00000000
## Q106042.fctrYes                           0.00000000   0.00000000
## Q106272.fctrNo                            0.00000000   0.00000000
## Q106272.fctrYes                           0.00000000   0.00000000
## Q106388.fctrNo                            0.00000000   0.00000000
## Q106388.fctrYes                           0.00000000   0.00000000
## Q106389.fctrNo                            0.00000000   0.00000000
## Q106389.fctrYes                           0.00000000   0.00000000
## Q106993.fctrNo                            0.00000000   0.00000000
## Q106993.fctrYes                           0.00000000   0.00000000
## Q106997.fctrGr                            0.00000000   0.00000000
## Q106997.fctrYy                            0.00000000   0.00000000
## Q107491.fctrNo                            0.00000000   0.00000000
## Q107491.fctrYes                           0.00000000   0.00000000
## Q107869.fctrNo                            0.00000000   0.00000000
## Q107869.fctrYes                           0.00000000   0.00000000
## Q108342.fctrIn-person                     0.00000000   0.00000000
## Q108342.fctrOnline                        0.00000000   0.00000000
## Q108343.fctrNo                            0.00000000   0.00000000
## Q108343.fctrYes                           0.00000000   0.00000000
## Q108617.fctrNo                            0.00000000   0.00000000
## Q108617.fctrYes                           0.00000000   0.00000000
## Q108754.fctrNo                            0.00000000   0.00000000
## Q108754.fctrYes                           0.00000000   0.00000000
## Q108855.fctrUmm...                        0.00000000   0.00000000
## Q108855.fctrYes!                          0.00000000   0.00000000
## Q108856.fctrSocialize                     0.00000000   0.00000000
## Q108856.fctrSpace                         0.00000000   0.00000000
## Q108950.fctrCautious                      0.00000000   0.00000000
## Q108950.fctrRisk-friendly                 0.00000000   0.00000000
## Q109367.fctrNo                            0.00000000   0.00000000
## Q109367.fctrYes                           0.00000000   0.00000000
## Q110740.fctrMac                           0.00000000   0.00000000
## Q111220.fctrNo                            0.00000000   0.00000000
## Q111220.fctrYes                           0.00000000   0.00000000
## Q111580.fctrDemanding                     0.00000000   0.00000000
## Q111580.fctrSupportive                    0.00000000   0.00000000
## Q111848.fctrNo                            0.00000000   0.00000000
## Q111848.fctrYes                           0.00000000   0.00000000
## Q112270.fctrNo                            0.00000000   0.00000000
## Q112270.fctrYes                           0.00000000   0.00000000
## Q112478.fctrNo                            0.00000000   0.00000000
## Q112478.fctrYes                           0.00000000   0.00000000
## Q112512.fctrNo                            0.00000000   0.00000000
## Q112512.fctrYes                           0.00000000   0.00000000
## Q113583.fctrTalk                          0.00000000   0.00000000
## Q113583.fctrTunes                         0.00000000   0.00000000
## Q113584.fctrPeople                        0.00000000   0.00000000
## Q113584.fctrTechnology                    0.00000000   0.00000000
## Q113992.fctrNo                            0.00000000   0.00000000
## Q113992.fctrYes                           0.00000000   0.00000000
## Q114152.fctrNo                            0.00000000   0.00000000
## Q114152.fctrYes                           0.00000000   0.00000000
## Q114386.fctrMysterious                    0.00000000   0.00000000
## Q114386.fctrTMI                           0.00000000   0.00000000
## Q114517.fctrNo                            0.00000000   0.00000000
## Q114517.fctrYes                           0.00000000   0.00000000
## Q114748.fctrNo                            0.00000000   0.00000000
## Q114748.fctrYes                           0.00000000   0.00000000
## Q114961.fctrNo                            0.00000000   0.00000000
## Q114961.fctrYes                           0.00000000   0.00000000
## Q115195.fctrNo                            0.00000000   0.00000000
## Q115195.fctrYes                           0.00000000   0.00000000
## Q115390.fctrNo                            0.00000000   0.00000000
## Q115390.fctrYes                           0.00000000   0.00000000
## Q115602.fctrNo                            0.00000000   0.00000000
## Q115602.fctrYes                           0.00000000   0.00000000
## Q115610.fctrNo                            0.00000000   0.00000000
## Q115610.fctrYes                           0.00000000   0.00000000
## Q115777.fctrEnd                           0.00000000   0.00000000
## Q115777.fctrStart                         0.00000000   0.00000000
## Q115899.fctrMe                            0.00000000   0.00000000
## Q116197.fctrA.M.                          0.00000000   0.00000000
## Q116197.fctrP.M.                          0.00000000   0.00000000
## Q116441.fctrNo                            0.00000000   0.00000000
## Q116441.fctrYes                           0.00000000   0.00000000
## Q116448.fctrNo                            0.00000000   0.00000000
## Q116448.fctrYes                           0.00000000   0.00000000
## Q116601.fctrNo                            0.00000000   0.00000000
## Q116601.fctrYes                           0.00000000   0.00000000
## Q116797.fctrNo                            0.00000000   0.00000000
## Q116797.fctrYes                           0.00000000   0.00000000
## Q116953.fctrNo                            0.00000000   0.00000000
## Q116953.fctrYes                           0.00000000   0.00000000
## Q117186.fctrCool headed                   0.00000000   0.00000000
## Q117186.fctrHot headed                    0.00000000   0.00000000
## Q117193.fctrOdd hours                     0.00000000   0.00000000
## Q117193.fctrStandard hours                0.00000000   0.00000000
## Q118117.fctrNo                            0.00000000   0.00000000
## Q118117.fctrYes                           0.00000000   0.00000000
## Q118232.fctrPr                            0.00000000   0.00000000
## Q118233.fctrNo                            0.00000000   0.00000000
## Q118233.fctrYes                           0.00000000   0.00000000
## Q118237.fctrNo                            0.00000000   0.00000000
## Q118237.fctrYes                           0.00000000   0.00000000
## Q118892.fctrNo                            0.00000000   0.00000000
## Q118892.fctrYes                           0.00000000   0.00000000
## Q119334.fctrNo                            0.00000000   0.00000000
## Q119334.fctrYes                           0.00000000   0.00000000
## Q119650.fctrGiving                        0.00000000   0.00000000
## Q119650.fctrReceiving                     0.00000000   0.00000000
## Q119851.fctrYes                           0.00000000   0.00000000
## Q120012.fctrNo                            0.00000000   0.00000000
## Q120012.fctrYes                           0.00000000   0.00000000
## Q120014.fctrNo                            0.00000000   0.00000000
## Q120014.fctrYes                           0.00000000   0.00000000
## Q120194.fctrStudy first                   0.00000000   0.00000000
## Q120194.fctrTry first                     0.00000000   0.00000000
## Q120472.fctrArt                           0.00000000   0.00000000
## Q120472.fctrScience                       0.00000000   0.00000000
## Q120650.fctrNo                            0.00000000   0.00000000
## Q120650.fctrYes                           0.00000000   0.00000000
## Q120978.fctrNo                            0.00000000   0.00000000
## Q120978.fctrYes                           0.00000000   0.00000000
## Q121011.fctrNo                            0.00000000   0.00000000
## Q121011.fctrYes                           0.00000000   0.00000000
## Q121699.fctrYes                           0.00000000   0.00000000
## Q121700.fctrNo                            0.00000000   0.00000000
## Q121700.fctrYes                           0.00000000   0.00000000
## Q122120.fctrNo                            0.00000000   0.00000000
## Q122120.fctrYes                           0.00000000   0.00000000
## Q122769.fctrNo                            0.00000000   0.00000000
## Q122769.fctrYes                           0.00000000   0.00000000
## Q122770.fctrNo                            0.00000000   0.00000000
## Q122770.fctrYes                           0.00000000   0.00000000
## Q122771.fctrPc                            0.00000000   0.00000000
## Q123464.fctrNo                            0.00000000   0.00000000
## Q123464.fctrYes                           0.00000000   0.00000000
## Q123621.fctrNo                            0.00000000   0.00000000
## Q123621.fctrYes                           0.00000000   0.00000000
## Q124122.fctrNo                            0.00000000   0.00000000
## Q124122.fctrYes                           0.00000000   0.00000000
## Q124742.fctrNo                            0.00000000   0.00000000
## Q124742.fctrYes                           0.00000000   0.00000000
## Q96024.fctrNo                             0.00000000   0.00000000
## Q96024.fctrYes                            0.00000000   0.00000000
## Q98059.fctrOnly-child                     0.00000000   0.00000000
## Q98059.fctrYes                            0.00000000   0.00000000
## Q98078.fctrNo                             0.00000000   0.00000000
## Q98078.fctrYes                            0.00000000   0.00000000
## Q98578.fctrNo                             0.00000000   0.00000000
## Q98578.fctrYes                            0.00000000   0.00000000
## Q98869.fctrYes                            0.00000000   0.00000000
## Q99480.fctrYes                            0.00000000   0.00000000
## Q99581.fctrNo                             0.00000000   0.00000000
## Q99581.fctrYes                            0.00000000   0.00000000
## Q99716.fctrNo                             0.00000000   0.00000000
## Q99716.fctrYes                            0.00000000   0.00000000
## Q99982.fctrCheck!                         0.00000000   0.00000000
## Q99982.fctrNope                           0.00000000   0.00000000
## YOB.Age.fctr.L                            0.00000000   0.00000000
## YOB.Age.fctr.Q                            0.00000000   0.00000000
## YOB.Age.fctr.C                            0.00000000   0.00000000
## YOB.Age.fctr^4                            0.00000000   0.00000000
## YOB.Age.fctr^5                            0.00000000   0.00000000
## YOB.Age.fctr^6                            0.00000000   0.00000000
## YOB.Age.fctr^7                            0.00000000   0.00000000
## YOB.Age.fctr^8                            0.00000000   0.00000000
## Hhold.fctrN:.clusterid.fctr2              0.00000000   0.00000000
## Hhold.fctrMKn:.clusterid.fctr2            0.00000000   0.00000000
## Hhold.fctrMKy:.clusterid.fctr2            0.00000000   0.00000000
## Hhold.fctrPKn:.clusterid.fctr2            0.00000000   0.00000000
## Hhold.fctrPKy:.clusterid.fctr2            0.00000000   0.00000000
## Hhold.fctrSKn:.clusterid.fctr2            0.00000000   0.00000000
## Hhold.fctrSKy:.clusterid.fctr2            0.00000000   0.00000000
## Hhold.fctrN:.clusterid.fctr3              0.00000000   0.00000000
## Hhold.fctrMKn:.clusterid.fctr3            0.00000000   0.00000000
## Hhold.fctrMKy:.clusterid.fctr3            0.00000000   0.00000000
## Hhold.fctrPKn:.clusterid.fctr3            0.00000000   0.00000000
## Hhold.fctrPKy:.clusterid.fctr3            0.00000000   0.00000000
## Hhold.fctrSKn:.clusterid.fctr3            0.00000000   0.00000000
## Hhold.fctrSKy:.clusterid.fctr3            0.00000000   0.00000000
## Hhold.fctrN:.clusterid.fctr4              0.00000000   0.00000000
## Hhold.fctrMKn:.clusterid.fctr4            0.00000000   0.00000000
## Hhold.fctrMKy:.clusterid.fctr4            0.00000000   0.00000000
## Hhold.fctrPKn:.clusterid.fctr4            0.00000000   0.00000000
## Hhold.fctrPKy:.clusterid.fctr4            0.00000000   0.00000000
## Hhold.fctrSKn:.clusterid.fctr4            0.00000000   0.00000000
## Hhold.fctrSKy:.clusterid.fctr4            0.00000000   0.00000000

#mdl_id <-"RFE.X.glmnet"; glb_featsimp_df <- myget_feats_importance(glb_models_lst[[mdl_id]], glb_featsimp_df); glb_featsimp_df[, paste0(mdl_id, ".imp")] <- glb_featsimp_df$imp; print(glb_featsimp_df)
#print(head(sbst_featsimp_df <- subset(glb_featsimp_df, is.na(RFE.X.glmnet.imp) | (abs(RFE.X.YeoJohnson.glmnet.imp - RFE.X.glmnet.imp) > 0.0001), select=-imp)))
#print(orderBy(~ -cor.y.abs, subset(glb_feats_df, id %in% c(row.names(sbst_featsimp_df), "startprice.dcm1.is9", "D.weight.post.stop.sum"))))

# Used again in fit.data.training & predict.data.new chunks
glb_analytics_diag_plots <- function(obs_df, mdl_id, prob_threshold=NULL) {
    if (!is.null(featsimp_df <- glb_featsimp_df)) {
        featsimp_df$feat <- gsub("`(.*?)`", "\\1", row.names(featsimp_df))    
        featsimp_df$feat.interact <- gsub("(.*?):(.*)", "\\2", featsimp_df$feat)
        featsimp_df$feat <- gsub("(.*?):(.*)", "\\1", featsimp_df$feat)    
        featsimp_df$feat.interact <- 
            ifelse(featsimp_df$feat.interact == featsimp_df$feat, 
                                            NA, featsimp_df$feat.interact)
        featsimp_df$feat <- 
            gsub("(.*?)\\.fctr(.*)", "\\1\\.fctr", featsimp_df$feat)
        featsimp_df$feat.interact <- 
            gsub("(.*?)\\.fctr(.*)", "\\1\\.fctr", featsimp_df$feat.interact) 
        featsimp_df <- orderBy(~ -imp.max, 
            summaryBy(imp ~ feat + feat.interact, data=featsimp_df,
                      FUN=max))    
        #rex_str=":(.*)"; txt_vctr=tail(featsimp_df$feat); ret_lst <- regexec(rex_str, txt_vctr); ret_lst <- regmatches(txt_vctr, ret_lst); ret_vctr <- sapply(1:length(ret_lst), function(pos_ix) ifelse(length(ret_lst[[pos_ix]]) > 0, ret_lst[[pos_ix]], "")); print(ret_vctr <- ret_vctr[ret_vctr != ""])    
        
        featsimp_df <- subset(featsimp_df, !is.na(imp.max))
        if (nrow(featsimp_df) > 5) {
            warning("Limiting important feature scatter plots to 5 out of ",
                    nrow(featsimp_df))
            featsimp_df <- head(featsimp_df, 5)
        }
        
    #     if (!all(is.na(featsimp_df$feat.interact)))
    #         stop("not implemented yet")
        rsp_var_out <- mygetPredictIds(glb_rsp_var, mdl_id)$value
        for (var in featsimp_df$feat) {
            plot_df <- melt(obs_df, id.vars = var, 
                            measure.vars = c(glb_rsp_var, rsp_var_out))
    
            print(myplot_scatter(plot_df, var, "value", colorcol_name = "variable",
                                facet_colcol_name = "variable", jitter = TRUE) + 
                          guides(color = FALSE))
        }
    }
    
    if (glb_is_regression) {
        if (is.null(featsimp_df) || (nrow(featsimp_df) == 0))
            warning("No important features in glb_fin_mdl") else
            print(myplot_prediction_regression(df=obs_df, 
                        feat_x=ifelse(nrow(featsimp_df) > 1, featsimp_df$feat[2],
                                      ".rownames"), 
                                               feat_y=featsimp_df$feat[1],
                        rsp_var=glb_rsp_var, rsp_var_out=rsp_var_out,
                        id_vars=glbFeatsId)
    #               + facet_wrap(reformulate(featsimp_df$feat[2])) # if [1 or 2] is a factor
    #               + geom_point(aes_string(color="<col_name>.fctr")) #  to color the plot
                  )
    }    
    
    if (glb_is_classification) {
        require(lazyeval)
        
        if (is.null(featsimp_df) || (nrow(featsimp_df) == 0))
            warning("No features in selected model are statistically important")
        else print(myplot_prediction_classification(df = obs_df, 
                                feat_x = ifelse(nrow(featsimp_df) > 1, 
                                                featsimp_df$feat[2], ".rownames"),
                                               feat_y = featsimp_df$feat[1],
                                                rsp_var = glb_rsp_var, 
                                                rsp_var_out = rsp_var_out, 
                                                id_vars = glbFeatsId,
                                                prob_threshold = prob_threshold))
    }    
}

if (glb_is_classification && glb_is_binomial)
    glb_analytics_diag_plots(obs_df = glbObsOOB, mdl_id = glbMdlSelId, 
            prob_threshold = glb_models_df[glb_models_df$id == glbMdlSelId, 
                                           "opt.prob.threshold.OOB"]) else
    glb_analytics_diag_plots(obs_df = glbObsOOB, mdl_id = glbMdlSelId)                  

## Warning in glb_analytics_diag_plots(obs_df = glbObsOOB, mdl_id =
## glbMdlSelId, : Limiting important feature scatter plots to 5 out of 108

## [1] "Min/Max Boundaries: "
## [1] "Inaccurate: "
##   USER_ID Party.fctr Party.fctr.All.X..rcv.glmnet.prob
## 1     943          D                         0.3112901
## 2    1393          D                         0.3191111
## 3    2798          D                         0.3202234
## 4    5028          D                         0.3268617
## 5    1843          D                         0.3293382
## 6    1045          D                         0.3293487
##   Party.fctr.All.X..rcv.glmnet Party.fctr.All.X..rcv.glmnet.err
## 1                            R                             TRUE
## 2                            R                             TRUE
## 3                            R                             TRUE
## 4                            R                             TRUE
## 5                            R                             TRUE
## 6                            R                             TRUE
##   Party.fctr.All.X..rcv.glmnet.err.abs Party.fctr.All.X..rcv.glmnet.is.acc
## 1                            0.6887099                               FALSE
## 2                            0.6808889                               FALSE
## 3                            0.6797766                               FALSE
## 4                            0.6731383                               FALSE
## 5                            0.6706618                               FALSE
## 6                            0.6706513                               FALSE
##   Party.fctr.All.X..rcv.glmnet.accurate Party.fctr.All.X..rcv.glmnet.error
## 1                                 FALSE                         -0.3887099
## 2                                 FALSE                         -0.3808889
## 3                                 FALSE                         -0.3797766
## 4                                 FALSE                         -0.3731383
## 5                                 FALSE                         -0.3706618
## 6                                 FALSE                         -0.3706513
##     USER_ID Party.fctr Party.fctr.All.X..rcv.glmnet.prob
## 12     3686          D                         0.3459625
## 106    4514          D                         0.4621860
## 206    6042          D                         0.5240140
## 250    1938          D                         0.5295261
## 307    6883          D                         0.5433422
## 449    1739          D                         0.6368048
##     Party.fctr.All.X..rcv.glmnet Party.fctr.All.X..rcv.glmnet.err
## 12                             R                             TRUE
## 106                            R                             TRUE
## 206                            R                             TRUE
## 250                            R                             TRUE
## 307                            R                             TRUE
## 449                            R                             TRUE
##     Party.fctr.All.X..rcv.glmnet.err.abs
## 12                             0.6540375
## 106                            0.5378140
## 206                            0.4759860
## 250                            0.4704739
## 307                            0.4566578
## 449                            0.3631952
##     Party.fctr.All.X..rcv.glmnet.is.acc
## 12                                FALSE
## 106                               FALSE
## 206                               FALSE
## 250                               FALSE
## 307                               FALSE
## 449                               FALSE
##     Party.fctr.All.X..rcv.glmnet.accurate
## 12                                  FALSE
## 106                                 FALSE
## 206                                 FALSE
## 250                                 FALSE
## 307                                 FALSE
## 449                                 FALSE
##     Party.fctr.All.X..rcv.glmnet.error
## 12                         -0.35403749
## 106                        -0.23781405
## 206                        -0.17598599
## 250                        -0.17047387
## 307                        -0.15665784
## 449                        -0.06319525
##     USER_ID Party.fctr Party.fctr.All.X..rcv.glmnet.prob
## 494    5466          R                         0.7868650
## 495     451          R                         0.7931354
## 496    1564          R                         0.7986553
## 497    3921          R                         0.7999019
## 498    4325          R                         0.8037402
## 499    1307          R                         0.8090310
##     Party.fctr.All.X..rcv.glmnet Party.fctr.All.X..rcv.glmnet.err
## 494                            D                             TRUE
## 495                            D                             TRUE
## 496                            D                             TRUE
## 497                            D                             TRUE
## 498                            D                             TRUE
## 499                            D                             TRUE
##     Party.fctr.All.X..rcv.glmnet.err.abs
## 494                            0.7868650
## 495                            0.7931354
## 496                            0.7986553
## 497                            0.7999019
## 498                            0.8037402
## 499                            0.8090310
##     Party.fctr.All.X..rcv.glmnet.is.acc
## 494                               FALSE
## 495                               FALSE
## 496                               FALSE
## 497                               FALSE
## 498                               FALSE
## 499                               FALSE
##     Party.fctr.All.X..rcv.glmnet.accurate
## 494                                 FALSE
## 495                                 FALSE
## 496                                 FALSE
## 497                                 FALSE
## 498                                 FALSE
## 499                                 FALSE
##     Party.fctr.All.X..rcv.glmnet.error
## 494                         0.08686496
## 495                         0.09313539
## 496                         0.09865532
## 497                         0.09990189
## 498                         0.10374022
## 499                         0.10903104

if (!is.null(glbFeatsCategory)) {
    glbLvlCategory <- merge(glbLvlCategory, 
            myget_category_stats(obs_df = glbObsFit, mdl_id = glbMdlSelId, 
                                 label = "fit"), 
                            by = glbFeatsCategory, all = TRUE)
    row.names(glbLvlCategory) <- glbLvlCategory[, glbFeatsCategory]
    glbLvlCategory <- merge(glbLvlCategory, 
            myget_category_stats(obs_df = glbObsOOB, mdl_id = glbMdlSelId,
                                 label="OOB"),
                          #by=glbFeatsCategory, all=TRUE) glb_ctgry-df already contains .n.OOB ?
                          all = TRUE)
    row.names(glbLvlCategory) <- glbLvlCategory[, glbFeatsCategory]
    if (any(grepl("OOB", glbMdlMetricsEval)))
        print(orderBy(~-err.abs.OOB.mean, glbLvlCategory)) else
            print(orderBy(~-err.abs.fit.mean, glbLvlCategory))
    print(colSums(glbLvlCategory[, -grep(glbFeatsCategory, names(glbLvlCategory))]))
}

##     Hhold.fctr .n.OOB .n.Fit .n.Tst .freqRatio.Fit .freqRatio.OOB
## PKy        PKy      9     52     10     0.01169065    0.008035714
## PKn        PKn     30    150     37     0.03372302    0.026785714
## N            N     83    367    102     0.08250899    0.074107143
## SKn        SKn    511   1920    638     0.43165468    0.456250000
## SKy        SKy     53    147     65     0.03304856    0.047321429
## MKn        MKn    136    516    169     0.11600719    0.121428571
## MKy        MKy    298   1296    371     0.29136691    0.266071429
##     .freqRatio.Tst err.abs.fit.sum err.abs.fit.mean .n.fit err.abs.OOB.sum
## PKy    0.007183908        24.71367        0.4752630     52         4.28726
## PKn    0.026580460        59.07825        0.3938550    150        14.10653
## N      0.073275862       173.80353        0.4735791    367        38.82857
## SKn    0.458333333       884.82425        0.4608460   1920       238.72729
## SKy    0.046695402        65.43207        0.4451161    147        24.55390
## MKn    0.121408046       235.21548        0.4558439    516        62.21564
## MKy    0.266522989       595.03188        0.4591295   1296       134.54661
##     err.abs.OOB.mean
## PKy        0.4763622
## PKn        0.4702178
## N          0.4678140
## SKn        0.4671767
## SKy        0.4632812
## MKn        0.4574680
## MKy        0.4514987
##           .n.OOB           .n.Fit           .n.Tst   .freqRatio.Fit 
##      1120.000000      4448.000000      1392.000000         1.000000 
##   .freqRatio.OOB   .freqRatio.Tst  err.abs.fit.sum err.abs.fit.mean 
##         1.000000         1.000000      2038.099138         3.163633 
##           .n.fit  err.abs.OOB.sum err.abs.OOB.mean 
##      4448.000000       517.265807         3.253819

write.csv(glbObsOOB[, c(glbFeatsId, 
                grep(glb_rsp_var, names(glbObsOOB), fixed=TRUE, value=TRUE))], 
    paste0(gsub(".", "_", paste0(glbOut$pfx, glbMdlSelId), fixed=TRUE), 
           "_OOBobs.csv"), row.names=FALSE)

fit.models_2_chunk_df <- 
    myadd_chunk(NULL, "fit.models_2_bgn", label.minor = "teardown")

##              label step_major step_minor label_minor     bgn end elapsed
## 1 fit.models_2_bgn          1          0    teardown 431.806  NA      NA

glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.models", major.inc=FALSE)

##        label step_major step_minor label_minor     bgn     end elapsed
## 6 fit.models          4          2           2 418.837 431.818  12.981
## 7 fit.models          4          3           3 431.818      NA      NA

# if (sum(is.na(glbObsAll$D.P.http)) > 0)
#         stop("fit.models_3: Why is this happening ?")

#stop(here"); glb2Sav()
sync_glb_obs_df <- function() {
    # Merge or cbind ?
    for (col in setdiff(names(glbObsFit), names(glbObsTrn)))
        glbObsTrn[glbObsTrn$.lcn == "Fit", col] <<- glbObsFit[, col]
    for (col in setdiff(names(glbObsFit), names(glbObsAll)))
        glbObsAll[glbObsAll$.lcn == "Fit", col] <<- glbObsFit[, col]
    if (all(is.na(glbObsNew[, glb_rsp_var])))
        for (col in setdiff(names(glbObsOOB), names(glbObsTrn)))
            glbObsTrn[glbObsTrn$.lcn == "OOB", col] <<- glbObsOOB[, col]
    for (col in setdiff(names(glbObsOOB), names(glbObsAll)))
        glbObsAll[glbObsAll$.lcn == "OOB", col] <<- glbObsOOB[, col]
}
sync_glb_obs_df()
    
print(setdiff(names(glbObsNew), names(glbObsAll)))

## character(0)

replay.petrisim(pn = glb_analytics_pn, 
    replay.trans = (glb_analytics_avl_objs <- c(glb_analytics_avl_objs, 
        "model.selected")), flip_coord = TRUE)

## time trans    "bgn " "fit.data.training.all " "predict.data.new " "end " 
## 0.0000   multiple enabled transitions:  data.training.all data.new model.selected    firing:  model.selected 
## 1.0000    3   2 1 0 0

glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.data.training", major.inc = TRUE)

##               label step_major step_minor label_minor     bgn    end
## 7        fit.models          4          3           3 431.818 436.54
## 8 fit.data.training          5          0           0 436.540     NA
##   elapsed
## 7   4.722
## 8      NA

Step 5.0: fit data training

#load(paste0(glb_inp_pfx, "dsk.RData"))

if (!is.null(glbMdlFinId) && (glbMdlFinId %in% names(glb_models_lst))) {
    warning("Final model same as user selected model")
    glb_fin_mdl <- glb_models_lst[[glbMdlFinId]]
} else 
# if (nrow(glbObsFit) + length(glbObsFitOutliers) == nrow(glbObsTrn))
if (!all(is.na(glbObsNew[, glb_rsp_var])))
{    
    warning("Final model same as glbMdlSelId")
    glbMdlFinId <- paste0("Final.", glbMdlSelId)
    glb_fin_mdl <- glb_sel_mdl
    glb_models_lst[[glbMdlFinId]] <- glb_fin_mdl
    mdlDf <- glb_models_df[glb_models_df$id == glbMdlSelId, ]
    mdlDf$id <- glbMdlFinId
    glb_models_df <- rbind(glb_models_df, mdlDf)
} else {    
            if (grepl("RFE\\.X", names(glbMdlFamilies))) {
                indepVar <- mygetIndepVar(glb_feats_df)
                rfe_trn_results <- 
                    myrun_rfe(glbObsTrn, indepVar, glbRFESizes[["Final"]])
                if (!isTRUE(all.equal(sort(predictors(rfe_trn_results)),
                                      sort(predictors(rfe_fit_results))))) {
                    print("Diffs predictors(rfe_trn_results) vs. predictors(rfe_fit_results):")
                    print(setdiff(predictors(rfe_trn_results), predictors(rfe_fit_results)))
                    print("Diffs predictors(rfe_fit_results) vs. predictors(rfe_trn_results):")
                    print(setdiff(predictors(rfe_fit_results), predictors(rfe_trn_results)))
            }
        }
    # }    

    if (grepl("Ensemble", glbMdlSelId)) {
        # Find which models are relevant
        mdlimp_df <- subset(myget_feats_importance(glb_sel_mdl), imp > 5)
        # Fit selected models on glbObsTrn
        for (mdl_id in gsub(".prob", "", 
gsub(mygetPredictIds(glb_rsp_var)$value, "", row.names(mdlimp_df), fixed = TRUE),
                            fixed = TRUE)) {
            mdl_id_components <- unlist(strsplit(mdl_id, "[.]"))
            mdlIdPfx <- paste0(c(head(mdl_id_components, -1), "Train"), 
                               collapse = ".")
            if (grepl("RFE\\.X\\.", mdlIdPfx)) 
                mdlIndepVars <- myadjustInteractionFeats(glb_feats_df, myextract_actual_feats(
                    predictors(rfe_trn_results))) else
                mdlIndepVars <- trim(unlist(
            strsplit(glb_models_df[glb_models_df$id == mdl_id, "feats"], "[,]")))
            ret_lst <- 
                myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
                        id.prefix = mdlIdPfx, 
                        type = glb_model_type, tune.df = glbMdlTuneParams,
                        trainControl.method = "repeatedcv",
                        trainControl.number = glb_rcv_n_folds,
                        trainControl.repeats = glb_rcv_n_repeats,
                        trainControl.classProbs = glb_is_classification,
                        trainControl.summaryFunction = glbMdlMetricSummaryFn,
                        train.metric = glbMdlMetricSummary, 
                        train.maximize = glbMdlMetricMaximize,    
                        train.method = tail(mdl_id_components, 1))),
                    indepVar = mdlIndepVars,
                    rsp_var = glb_rsp_var, 
                    fit_df = glbObsTrn, OOB_df = NULL)
            
            glbObsTrn <- glb_get_predictions(df = glbObsTrn,
                                                mdl_id = tail(glb_models_df$id, 1), 
                                                rsp_var = glb_rsp_var,
                                                prob_threshold_def = 
                    subset(glb_models_df, id == mdl_id)$opt.prob.threshold.OOB)
            glbObsNew <- glb_get_predictions(df = glbObsNew,
                                                mdl_id = tail(glb_models_df$id, 1), 
                                                rsp_var = glb_rsp_var,
                                                prob_threshold_def = 
                    subset(glb_models_df, id == mdl_id)$opt.prob.threshold.OOB)
        }    
    }
    
    # "Final" model
    if ((model_method <- glb_sel_mdl$method) == "custom")
        # get actual method from the mdl_id
        model_method <- tail(unlist(strsplit(glbMdlSelId, "[.]")), 1)
        
    if (grepl("Ensemble", glbMdlSelId)) {
        # Find which models are relevant
        mdlimp_df <- subset(myget_feats_importance(glb_sel_mdl), imp > 5)
        if (glb_is_classification && glb_is_binomial)
            indepVar <- gsub("(.*)\\.(.*)\\.prob", "\\1\\.Train\\.\\2\\.prob",
                                    row.names(mdlimp_df)) else
            indepVar <- gsub("(.*)\\.(.*)", "\\1\\.Train\\.\\2",
                                    row.names(mdlimp_df))
    } else 
    if (grepl("RFE.X", glbMdlSelId, fixed = TRUE)) {
        indepVar <- myextract_actual_feats(predictors(rfe_trn_results))
    } else indepVar <- 
                trim(unlist(strsplit(glb_models_df[glb_models_df$id ==
                                                   glbMdlSelId
                                                   , "feats"], "[,]")))
        
    if (!is.null(glb_preproc_methods) &&
        ((match_pos <- regexpr(gsub(".", "\\.", 
                                    paste(glb_preproc_methods, collapse = "|"),
                                   fixed = TRUE), glbMdlSelId)) != -1))
        ths_preProcess <- str_sub(glbMdlSelId, match_pos, 
                                match_pos + attr(match_pos, "match.length") - 1) else
        ths_preProcess <- NULL                                      

    mdl_id_pfx <- ifelse(grepl("Ensemble", glbMdlSelId),
                                   "Final.Ensemble", "Final")
    
    trnobs_df <- glbObsTrn 
    if (!is.null(glbObsTrnOutliers[[mdl_id_pfx]])) {
        trnobs_df <- glbObsTrn[!(glbObsTrn[, glbFeatsId] %in% glbObsTrnOutliers[[mdl_id_pfx]]), ]
        print(sprintf("Outliers removed: %d", nrow(glbObsTrn) - nrow(trnobs_df)))
        print(setdiff(glbObsTrn[, glbFeatsId], trnobs_df[, glbFeatsId]))
    }    
        
    # Force fitting of Final.glm to identify outliers
    method_vctr <- unique(c(myparseMdlId(glbMdlSelId)$alg, glbMdlFamilies[["Final"]]))
    for (method in method_vctr) {
        #source("caret_nominalTrainWorkflow.R")
        
        # glmnet requires at least 2 indep vars
        if ((length(indepVar) == 1) && (method %in% "glmnet"))
            next
        
        ret_lst <- 
            myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
                    id.prefix = mdl_id_pfx, 
                    type = glb_model_type, trainControl.method = "repeatedcv",
                    trainControl.number = glb_rcv_n_folds, 
                    trainControl.repeats = glb_rcv_n_repeats,
                    trainControl.classProbs = glb_is_classification,
                    trainControl.summaryFunction = glbMdlMetricSummaryFn,
                    trainControl.allowParallel = glbMdlAllowParallel,
                    train.metric = glbMdlMetricSummary, 
                    train.maximize = glbMdlMetricMaximize,    
                    train.method = method,
                    train.preProcess = ths_preProcess)),
                indepVar = indepVar, rsp_var = glb_rsp_var, 
                fit_df = trnobs_df, OOB_df = NULL)
        
        if ((length(method_vctr) == 1) || (method != "glm")) {
            glb_fin_mdl <- glb_models_lst[[length(glb_models_lst)]] 
            glbMdlFinId <- glb_models_df[length(glb_models_lst), "id"]
        }
    }
        
}

## [1] "myfit_mdl: enter: 0.000000 secs"
## [1] "fitting model: Final##rcv#glmnet"
## [1] "    indepVar: Q109244.fctr,Hhold.fctr,Edn.fctr,Q101163.fctr,Q100689.fctr,Q98078.fctr,Q99716.fctr,Q120379.fctr,Q121699.fctr,Q105840.fctr,Q113583.fctr,Q115195.fctr,Q102089.fctr,Q98059.fctr,Q114386.fctr,Q100680.fctr,Q108342.fctr,Q111848.fctr,YOB.Age.fctr,Q118892.fctr,Q102687.fctr,Q115390.fctr,Q119851.fctr,Q114517.fctr,Q120012.fctr,Q109367.fctr,Q114961.fctr,Q121700.fctr,Q124122.fctr,Q111220.fctr,Q113992.fctr,Q121011.fctr,Q106042.fctr,Q116448.fctr,Q116601.fctr,Q104996.fctr,Q102906.fctr,Q113584.fctr,Q108950.fctr,Q102674.fctr,Q103293.fctr,Q112478.fctr,Q114748.fctr,Q107491.fctr,Q100562.fctr,Q108617.fctr,Q100010.fctr,Q115602.fctr,Q116953.fctr,Q115610.fctr,Q106997.fctr,Q120978.fctr,Q112512.fctr,Q108343.fctr,Q96024.fctr,Q106389.fctr,.rnorm,Q108754.fctr,Q98578.fctr,Q101162.fctr,Q115777.fctr,Q99581.fctr,Q124742.fctr,Q116797.fctr,Q112270.fctr,Q118237.fctr,Q119650.fctr,Q111580.fctr,Q123464.fctr,Q117193.fctr,Q99982.fctr,Q108856.fctr,Q118233.fctr,Q102289.fctr,Q116197.fctr,Income.fctr,Q118232.fctr,Q120194.fctr,Q114152.fctr,Q122770.fctr,Q117186.fctr,Q105655.fctr,Q106993.fctr,Q119334.fctr,Q122120.fctr,Q116441.fctr,Q118117.fctr,Q123621.fctr,Q122769.fctr,Q120650.fctr,Q98869.fctr,Q107869.fctr,Q120014.fctr,Q115899.fctr,Q106388.fctr,Q99480.fctr,Q122771.fctr,Q108855.fctr,Q110740.fctr,Q106272.fctr,Q101596.fctr,Q116881.fctr,Q120472.fctr,Q98197.fctr,Q113181.fctr,Q115611.fctr,Gender.fctr,Hhold.fctr:.clusterid.fctr"
## [1] "myfit_mdl: setup complete: 0.748000 secs"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.775, lambda = 0.0113 on full training set
## [1] "myfit_mdl: train complete: 29.352000 secs"

##             Length Class      Mode     
## a0             77  -none-     numeric  
## beta        19481  dgCMatrix  S4       
## df             77  -none-     numeric  
## dim             2  -none-     numeric  
## lambda         77  -none-     numeric  
## dev.ratio      77  -none-     numeric  
## nulldev         1  -none-     numeric  
## npasses         1  -none-     numeric  
## jerr            1  -none-     numeric  
## offset          1  -none-     logical  
## classnames      2  -none-     character
## call            5  -none-     call     
## nobs            1  -none-     numeric  
## lambdaOpt       1  -none-     numeric  
## xNames        253  -none-     character
## problemType     1  -none-     character
## tuneValue       2  data.frame list     
## obsLevels       2  -none-     character
## [1] "min lambda > lambdaOpt:"
##                    (Intercept)                     Edn.fctr.L 
##                   2.138156e-01                   2.156619e-02 
##                   Gender.fctrM                  Hhold.fctrMKy 
##                  -1.089679e-01                  -8.270664e-02 
##                  Hhold.fctrPKn                  Income.fctr.Q 
##                   3.765391e-01                  -5.986343e-02 
##                  Income.fctr.C                Q100689.fctrYes 
##                  -6.813290e-02                   7.783887e-02 
##                Q101163.fctrDad                Q101163.fctrMom 
##                  -9.462329e-02                   5.472378e-02 
##                 Q104996.fctrNo                 Q106042.fctrNo 
##                  -2.133251e-02                  -2.676220e-02 
##                 Q106389.fctrNo                 Q106997.fctrGr 
##                  -2.239212e-02                  -7.205404e-02 
##               Q108855.fctrYes!                 Q109244.fctrNo 
##                  -2.698117e-02                  -3.581687e-01 
##                Q109244.fctrYes                Q110740.fctrMac 
##                   9.238602e-01                   2.166632e-02 
##                 Q110740.fctrPC                 Q112478.fctrNo 
##                  -7.131352e-02                  -3.211468e-02 
##                 Q113181.fctrNo                Q113181.fctrYes 
##                   1.047110e-01                  -1.442151e-01 
##                Q115195.fctrYes                 Q115390.fctrNo 
##                   1.405475e-02                  -6.189939e-03 
##                Q115390.fctrYes                 Q115611.fctrNo 
##                   5.539365e-02                   1.360572e-01 
##                Q115611.fctrYes                 Q115899.fctrCs 
##                  -3.311599e-01                   4.770838e-02 
##              Q116881.fctrHappy              Q116881.fctrRight 
##                   1.613896e-02                  -1.669034e-01 
##                 Q116953.fctrNo                 Q118232.fctrId 
##                  -3.122223e-02                   9.788058e-02 
##                 Q118233.fctrNo                 Q119851.fctrNo 
##                  -4.138692e-03                  -1.006570e-01 
##        Q120194.fctrStudy first                 Q120379.fctrNo 
##                   4.019387e-02                  -1.255611e-02 
##                Q120379.fctrYes            Q120472.fctrScience 
##                   8.601638e-02                  -8.050083e-02 
##                Q120650.fctrYes                Q121699.fctrYes 
##                  -1.803593e-05                   3.297460e-02 
##                Q122120.fctrYes                 Q122771.fctrPt 
##                  -1.403532e-02                  -7.590157e-02 
##                 Q124742.fctrNo                  Q98197.fctrNo 
##                   8.225250e-03                   2.843093e-01 
##                 Q98197.fctrYes                  Q98869.fctrNo 
##                  -3.393199e-03                   1.882597e-01 
##                  Q99480.fctrNo                 Q99480.fctrYes 
##                   3.587746e-02                  -4.842128e-02 
##                 YOB.Age.fctr.L                 YOB.Age.fctr^7 
##                   6.253624e-02                  -1.171636e-02 
##                 YOB.Age.fctr^8 Hhold.fctrPKy:.clusterid.fctr2 
##                  -3.305365e-02                  -1.423205e-01 
## [1] "max lambda < lambdaOpt:"
##                    (Intercept)                     Edn.fctr.L 
##                    0.221557383                    0.025058053 
##                   Gender.fctrM                  Hhold.fctrMKy 
##                   -0.111140351                   -0.093786180 
##                  Hhold.fctrPKn                  Income.fctr.Q 
##                    0.387162882                   -0.067657351 
##                  Income.fctr.C                Q100689.fctrYes 
##                   -0.081076253                    0.087471734 
##                Q101163.fctrDad                Q101163.fctrMom 
##                   -0.097215675                    0.060511585 
##                 Q104996.fctrNo                 Q106042.fctrNo 
##                   -0.029571989                   -0.029240474 
##                 Q106389.fctrNo                 Q106997.fctrGr 
##                   -0.031046021                   -0.081019110 
##               Q108855.fctrYes!                 Q109244.fctrNo 
##                   -0.034713886                   -0.359888700 
##                Q109244.fctrYes                Q110740.fctrMac 
##                    0.928369409                    0.028861981 
##                 Q110740.fctrPC                Q111220.fctrYes 
##                   -0.073464984                    0.004410685 
##                 Q112478.fctrNo                 Q113181.fctrNo 
##                   -0.040175275                    0.109523427 
##                Q113181.fctrYes                Q115195.fctrYes 
##                   -0.142630836                    0.022498392 
##                 Q115390.fctrNo                Q115390.fctrYes 
##                   -0.015335151                    0.057684572 
##                 Q115611.fctrNo                Q115611.fctrYes 
##                    0.136955264                   -0.336734882 
##                 Q115899.fctrCs              Q116881.fctrHappy 
##                    0.055929955                    0.025614243 
##              Q116881.fctrRight                 Q116953.fctrNo 
##                   -0.167242989                   -0.041754662 
##                 Q118232.fctrId                 Q118233.fctrNo 
##                    0.108684459                   -0.015024757 
##                 Q119851.fctrNo        Q120194.fctrStudy first 
##                   -0.106937552                    0.049745993 
##                 Q120379.fctrNo                Q120379.fctrYes 
##                   -0.011127780                    0.098534948 
##            Q120472.fctrScience                Q120650.fctrYes 
##                   -0.087008878                   -0.011347873 
##                Q121699.fctrYes                Q122120.fctrYes 
##                    0.041926076                   -0.023285019 
##                 Q122771.fctrPt                 Q124742.fctrNo 
##                   -0.087185780                    0.021110646 
##                  Q98197.fctrNo                  Q98869.fctrNo 
##                    0.293275290                    0.198055095 
##                  Q99480.fctrNo                 Q99480.fctrYes 
##                    0.036506583                   -0.059490412 
##                 YOB.Age.fctr.L                 YOB.Age.fctr^7 
##                    0.077876813                   -0.024154041 
##                 YOB.Age.fctr^8 Hhold.fctrPKy:.clusterid.fctr2 
##                   -0.045747091                   -0.199268173 
## [1] "myfit_mdl: train diagnostics complete: 30.045000 secs"

##          Prediction
## Reference    R    D
##         R 2322  295
##         D 1902 1049
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   6.054239e-01   2.345959e-01   5.924436e-01   6.182935e-01   5.299928e-01 
## AccuracyPValue  McnemarPValue 
##   5.228071e-30  2.754424e-257 
## [1] "myfit_mdl: predict complete: 37.739000 secs"
##                  id
## 1 Final##rcv#glmnet
##                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               feats
## 1 Q109244.fctr,Hhold.fctr,Edn.fctr,Q101163.fctr,Q100689.fctr,Q98078.fctr,Q99716.fctr,Q120379.fctr,Q121699.fctr,Q105840.fctr,Q113583.fctr,Q115195.fctr,Q102089.fctr,Q98059.fctr,Q114386.fctr,Q100680.fctr,Q108342.fctr,Q111848.fctr,YOB.Age.fctr,Q118892.fctr,Q102687.fctr,Q115390.fctr,Q119851.fctr,Q114517.fctr,Q120012.fctr,Q109367.fctr,Q114961.fctr,Q121700.fctr,Q124122.fctr,Q111220.fctr,Q113992.fctr,Q121011.fctr,Q106042.fctr,Q116448.fctr,Q116601.fctr,Q104996.fctr,Q102906.fctr,Q113584.fctr,Q108950.fctr,Q102674.fctr,Q103293.fctr,Q112478.fctr,Q114748.fctr,Q107491.fctr,Q100562.fctr,Q108617.fctr,Q100010.fctr,Q115602.fctr,Q116953.fctr,Q115610.fctr,Q106997.fctr,Q120978.fctr,Q112512.fctr,Q108343.fctr,Q96024.fctr,Q106389.fctr,.rnorm,Q108754.fctr,Q98578.fctr,Q101162.fctr,Q115777.fctr,Q99581.fctr,Q124742.fctr,Q116797.fctr,Q112270.fctr,Q118237.fctr,Q119650.fctr,Q111580.fctr,Q123464.fctr,Q117193.fctr,Q99982.fctr,Q108856.fctr,Q118233.fctr,Q102289.fctr,Q116197.fctr,Income.fctr,Q118232.fctr,Q120194.fctr,Q114152.fctr,Q122770.fctr,Q117186.fctr,Q105655.fctr,Q106993.fctr,Q119334.fctr,Q122120.fctr,Q116441.fctr,Q118117.fctr,Q123621.fctr,Q122769.fctr,Q120650.fctr,Q98869.fctr,Q107869.fctr,Q120014.fctr,Q115899.fctr,Q106388.fctr,Q99480.fctr,Q122771.fctr,Q108855.fctr,Q110740.fctr,Q106272.fctr,Q101596.fctr,Q116881.fctr,Q120472.fctr,Q98197.fctr,Q113181.fctr,Q115611.fctr,Gender.fctr,Hhold.fctr:.clusterid.fctr
##   max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1              25                     28.468                 2.488
##   max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1       0.6423128    0.5716469    0.7129787       0.2934485
##   opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1                    0.6       0.6788481           0.6335
##   max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1             0.5924436             0.6182935     0.2608147
##   max.AccuracySD.fit max.KappaSD.fit
## 1         0.01406455      0.02842487
## [1] "myfit_mdl: exit: 37.755000 secs"

rm(ret_lst)
glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.data.training", major.inc=FALSE)

##               label step_major step_minor label_minor     bgn     end
## 8 fit.data.training          5          0           0 436.540 474.918
## 9 fit.data.training          5          1           1 474.919      NA
##   elapsed
## 8  38.378
## 9      NA

#stop(here"); glb2Sav()
if (glb_is_classification && glb_is_binomial) 
    prob_threshold <- glb_models_df[glb_models_df$id == glbMdlSelId,
                                        "opt.prob.threshold.OOB"] else 
    prob_threshold <- NULL

if (grepl("Ensemble", glbMdlFinId)) {
    # Get predictions for each model in ensemble; Outliers that have been moved to OOB might not have been predicted yet
    mdlEnsembleComps <- unlist(str_split(subset(glb_models_df, 
                                                id == glbMdlFinId)$feats, ","))
    if (glb_is_classification && glb_is_binomial)
        mdlEnsembleComps <- gsub("\\.prob$", "", mdlEnsembleComps)
    mdlEnsembleComps <- gsub(paste0("^", 
                        gsub(".", "\\.", mygetPredictIds(glb_rsp_var)$value, fixed = TRUE)),
                             "", mdlEnsembleComps)
    for (mdl_id in mdlEnsembleComps) {
        glbObsTrn <- glb_get_predictions(df = glbObsTrn, mdl_id = mdl_id, 
                                            rsp_var = glb_rsp_var,
                                            prob_threshold_def = prob_threshold)
        glbObsNew <- glb_get_predictions(df = glbObsNew, mdl_id = mdl_id, 
                                            rsp_var = glb_rsp_var,
                                            prob_threshold_def = prob_threshold)
    }    
}
glbObsTrn <- glb_get_predictions(df = glbObsTrn, mdl_id = glbMdlFinId, 
                                     rsp_var = glb_rsp_var,
                                    prob_threshold_def = prob_threshold)

## Warning in glb_get_predictions(df = glbObsTrn, mdl_id = glbMdlFinId,
## rsp_var = glb_rsp_var, : Using default probability threshold: 0.7

glb_featsimp_df <- myget_feats_importance(mdl=glb_fin_mdl,
                                          featsimp_df=glb_featsimp_df)
#glb_featsimp_df[, paste0(glbMdlFinId, ".imp")] <- glb_featsimp_df$imp
print(glb_featsimp_df)

##                                All.X..rcv.glmnet.imp Final..rcv.glmnet.imp
## Q109244.fctrYes                         100.00000000           100.0000000
## Hhold.fctrPKn                            41.56642724            41.0410075
## Q109244.fctrNo                           39.80933610            38.7678006
## Q115611.fctrYes                          38.97373582            35.9731355
## Q98197.fctrNo                            20.63341630            31.0189010
## Q98869.fctrNo                            20.36490400            20.6642944
## Q116881.fctrRight                        10.34339619            18.0505253
## Hhold.fctrPKy:.clusterid.fctr2            0.00000000            17.2223975
## Q113181.fctrYes                          10.03215580            15.5361345
## Q115611.fctrNo                            9.69659664            14.7345916
## Gender.fctrM                              7.31671883            11.8478519
## Q113181.fctrNo                            4.47059621            11.4730392
## Q119851.fctrNo                            3.95883221            11.0822990
## Q118232.fctrId                            2.50949804            10.9283556
## Q101163.fctrDad                           5.93777650            10.3109992
## Q120379.fctrYes                           3.59678995             9.7014256
## Hhold.fctrMKy                             8.30653632             9.2972045
## Q120472.fctrScience                       0.00000000             8.9110969
## Q100689.fctrYes                           0.00000000             8.7243386
## Q122771.fctrPt                            0.01539812             8.5682993
## Q106997.fctrGr                            0.00000000             8.0775173
## Income.fctr.C                             0.00000000             7.7822353
## Q110740.fctrPC                            1.70814173             7.7773456
## YOB.Age.fctr.L                            0.00000000             7.2547765
## Income.fctr.Q                             0.05099013             6.7220721
## Q101163.fctrMom                           4.60717672             6.1017460
## Q115390.fctrYes                           0.00000000             6.0611708
## Q99480.fctrYes                            0.00000000             5.5911761
## Q115899.fctrCs                            0.01891999             5.4221246
## Q120194.fctrStudy first                   0.00000000             4.6529152
## YOB.Age.fctr^8                            0.00000000             3.9826628
## Q99480.fctrNo                             7.40277198             3.8980979
## Q121699.fctrYes                           0.00000000             3.8532235
## Q112478.fctrNo                            0.00000000             3.7314974
## Q116953.fctrNo                            0.00000000             3.7148984
## Q108855.fctrYes!                          0.00000000             3.1660550
## Q106042.fctrNo                            0.00000000             2.9726280
## Q106389.fctrNo                            0.00000000             2.6998149
## Q110740.fctrMac                           0.00000000             2.5742551
## Q104996.fctrNo                            0.00000000             2.5718993
## Edn.fctr.L                                0.00000000             2.4437702
## Q116881.fctrHappy                         1.26997411             2.0504868
## Q122120.fctrYes                           0.00000000             1.8158289
## Q115195.fctrYes                           0.00000000             1.7918873
## YOB.Age.fctr^7                            0.00000000             1.6681842
## Q120379.fctrNo                            0.14882265             1.3109658
## Q124742.fctrNo                            0.00000000             1.3053165
## Q115390.fctrNo                            0.00000000             0.9644949
## Q118233.fctrNo                            0.00000000             0.7990316
## Q120650.fctrYes                           0.00000000             0.3679928
## Q98197.fctrYes                            4.27686282             0.2571226
## Q111220.fctrYes                           0.00000000             0.1425001
## .rnorm                                    0.00000000             0.0000000
## Edn.fctr.C                                0.00000000             0.0000000
## Edn.fctr.Q                                0.00000000             0.0000000
## Edn.fctr^4                                0.00000000             0.0000000
## Edn.fctr^5                                0.00000000             0.0000000
## Edn.fctr^6                                0.00000000             0.0000000
## Edn.fctr^7                                0.00000000             0.0000000
## Gender.fctrF                              0.00000000             0.0000000
## Hhold.fctrMKn                             0.00000000             0.0000000
## Hhold.fctrMKn:.clusterid.fctr2            0.00000000             0.0000000
## Hhold.fctrMKn:.clusterid.fctr3            0.00000000             0.0000000
## Hhold.fctrMKn:.clusterid.fctr4            0.00000000             0.0000000
## Hhold.fctrMKy:.clusterid.fctr2            0.00000000             0.0000000
## Hhold.fctrMKy:.clusterid.fctr3            0.00000000             0.0000000
## Hhold.fctrMKy:.clusterid.fctr4            0.00000000             0.0000000
## Hhold.fctrN:.clusterid.fctr2              0.00000000             0.0000000
## Hhold.fctrN:.clusterid.fctr3              0.00000000             0.0000000
## Hhold.fctrN:.clusterid.fctr4              0.00000000             0.0000000
## Hhold.fctrPKn:.clusterid.fctr2            0.00000000             0.0000000
## Hhold.fctrPKn:.clusterid.fctr3            0.00000000             0.0000000
## Hhold.fctrPKn:.clusterid.fctr4            0.00000000             0.0000000
## Hhold.fctrPKy                             0.00000000             0.0000000
## Hhold.fctrPKy:.clusterid.fctr3            0.00000000             0.0000000
## Hhold.fctrPKy:.clusterid.fctr4            0.00000000             0.0000000
## Hhold.fctrSKn                             0.00000000             0.0000000
## Hhold.fctrSKn:.clusterid.fctr2            0.00000000             0.0000000
## Hhold.fctrSKn:.clusterid.fctr3            0.00000000             0.0000000
## Hhold.fctrSKn:.clusterid.fctr4            0.00000000             0.0000000
## Hhold.fctrSKy                             0.00000000             0.0000000
## Hhold.fctrSKy:.clusterid.fctr2            0.00000000             0.0000000
## Hhold.fctrSKy:.clusterid.fctr3            0.00000000             0.0000000
## Hhold.fctrSKy:.clusterid.fctr4            0.00000000             0.0000000
## Income.fctr.L                             0.00000000             0.0000000
## Income.fctr^4                             0.00000000             0.0000000
## Income.fctr^5                             0.00000000             0.0000000
## Income.fctr^6                             0.00000000             0.0000000
## Q100010.fctrNo                            0.00000000             0.0000000
## Q100010.fctrYes                           0.00000000             0.0000000
## Q100562.fctrNo                            0.00000000             0.0000000
## Q100562.fctrYes                           0.00000000             0.0000000
## Q100680.fctrNo                            0.00000000             0.0000000
## Q100680.fctrYes                           0.00000000             0.0000000
## Q100689.fctrNo                            0.00000000             0.0000000
## Q101162.fctrOptimist                      0.00000000             0.0000000
## Q101162.fctrPessimist                     0.00000000             0.0000000
## Q101596.fctrNo                            0.00000000             0.0000000
## Q101596.fctrYes                           0.00000000             0.0000000
## Q102089.fctrOwn                           0.00000000             0.0000000
## Q102089.fctrRent                          0.00000000             0.0000000
## Q102289.fctrNo                            0.00000000             0.0000000
## Q102289.fctrYes                           0.00000000             0.0000000
## Q102674.fctrNo                            0.00000000             0.0000000
## Q102674.fctrYes                           0.00000000             0.0000000
## Q102687.fctrNo                            0.00000000             0.0000000
## Q102687.fctrYes                           0.00000000             0.0000000
## Q102906.fctrNo                            0.00000000             0.0000000
## Q102906.fctrYes                           0.00000000             0.0000000
## Q103293.fctrNo                            0.00000000             0.0000000
## Q103293.fctrYes                           0.00000000             0.0000000
## Q104996.fctrYes                           0.00000000             0.0000000
## Q105655.fctrNo                            0.00000000             0.0000000
## Q105655.fctrYes                           0.00000000             0.0000000
## Q105840.fctrNo                            0.00000000             0.0000000
## Q105840.fctrYes                           0.00000000             0.0000000
## Q106042.fctrYes                           0.00000000             0.0000000
## Q106272.fctrNo                            0.00000000             0.0000000
## Q106272.fctrYes                           0.00000000             0.0000000
## Q106388.fctrNo                            0.00000000             0.0000000
## Q106388.fctrYes                           0.00000000             0.0000000
## Q106389.fctrYes                           0.00000000             0.0000000
## Q106993.fctrNo                            0.00000000             0.0000000
## Q106993.fctrYes                           0.00000000             0.0000000
## Q106997.fctrYy                            0.00000000             0.0000000
## Q107491.fctrNo                            0.00000000             0.0000000
## Q107491.fctrYes                           0.00000000             0.0000000
## Q107869.fctrNo                            0.00000000             0.0000000
## Q107869.fctrYes                           0.00000000             0.0000000
## Q108342.fctrIn-person                     0.00000000             0.0000000
## Q108342.fctrOnline                        0.00000000             0.0000000
## Q108343.fctrNo                            0.00000000             0.0000000
## Q108343.fctrYes                           0.00000000             0.0000000
## Q108617.fctrNo                            0.00000000             0.0000000
## Q108617.fctrYes                           0.00000000             0.0000000
## Q108754.fctrNo                            0.00000000             0.0000000
## Q108754.fctrYes                           0.00000000             0.0000000
## Q108855.fctrUmm...                        0.00000000             0.0000000
## Q108856.fctrSocialize                     0.00000000             0.0000000
## Q108856.fctrSpace                         0.00000000             0.0000000
## Q108950.fctrCautious                      0.00000000             0.0000000
## Q108950.fctrRisk-friendly                 0.00000000             0.0000000
## Q109367.fctrNo                            0.00000000             0.0000000
## Q109367.fctrYes                           0.00000000             0.0000000
## Q111220.fctrNo                            0.00000000             0.0000000
## Q111580.fctrDemanding                     0.00000000             0.0000000
## Q111580.fctrSupportive                    0.00000000             0.0000000
## Q111848.fctrNo                            0.00000000             0.0000000
## Q111848.fctrYes                           0.00000000             0.0000000
## Q112270.fctrNo                            0.00000000             0.0000000
## Q112270.fctrYes                           0.00000000             0.0000000
## Q112478.fctrYes                           0.00000000             0.0000000
## Q112512.fctrNo                            0.00000000             0.0000000
## Q112512.fctrYes                           0.00000000             0.0000000
## Q113583.fctrTalk                          0.00000000             0.0000000
## Q113583.fctrTunes                         0.00000000             0.0000000
## Q113584.fctrPeople                        0.00000000             0.0000000
## Q113584.fctrTechnology                    0.00000000             0.0000000
## Q113992.fctrNo                            0.00000000             0.0000000
## Q113992.fctrYes                           0.00000000             0.0000000
## Q114152.fctrNo                            0.00000000             0.0000000
## Q114152.fctrYes                           0.00000000             0.0000000
## Q114386.fctrMysterious                    0.00000000             0.0000000
## Q114386.fctrTMI                           0.00000000             0.0000000
## Q114517.fctrNo                            0.00000000             0.0000000
## Q114517.fctrYes                           0.00000000             0.0000000
## Q114748.fctrNo                            0.00000000             0.0000000
## Q114748.fctrYes                           0.00000000             0.0000000
## Q114961.fctrNo                            0.00000000             0.0000000
## Q114961.fctrYes                           0.00000000             0.0000000
## Q115195.fctrNo                            0.00000000             0.0000000
## Q115602.fctrNo                            0.00000000             0.0000000
## Q115602.fctrYes                           0.00000000             0.0000000
## Q115610.fctrNo                            0.00000000             0.0000000
## Q115610.fctrYes                           0.00000000             0.0000000
## Q115777.fctrEnd                           0.00000000             0.0000000
## Q115777.fctrStart                         0.00000000             0.0000000
## Q115899.fctrMe                            0.00000000             0.0000000
## Q116197.fctrA.M.                          0.00000000             0.0000000
## Q116197.fctrP.M.                          0.00000000             0.0000000
## Q116441.fctrNo                            0.00000000             0.0000000
## Q116441.fctrYes                           0.00000000             0.0000000
## Q116448.fctrNo                            0.00000000             0.0000000
## Q116448.fctrYes                           0.00000000             0.0000000
## Q116601.fctrNo                            0.00000000             0.0000000
## Q116601.fctrYes                           0.00000000             0.0000000
## Q116797.fctrNo                            0.00000000             0.0000000
## Q116797.fctrYes                           0.00000000             0.0000000
## Q116953.fctrYes                           0.00000000             0.0000000
## Q117186.fctrCool headed                   0.00000000             0.0000000
## Q117186.fctrHot headed                    0.00000000             0.0000000
## Q117193.fctrOdd hours                     0.00000000             0.0000000
## Q117193.fctrStandard hours                0.00000000             0.0000000
## Q118117.fctrNo                            0.00000000             0.0000000
## Q118117.fctrYes                           0.00000000             0.0000000
## Q118232.fctrPr                            0.00000000             0.0000000
## Q118233.fctrYes                           0.00000000             0.0000000
## Q118237.fctrNo                            0.00000000             0.0000000
## Q118237.fctrYes                           0.00000000             0.0000000
## Q118892.fctrNo                            0.00000000             0.0000000
## Q118892.fctrYes                           0.00000000             0.0000000
## Q119334.fctrNo                            0.00000000             0.0000000
## Q119334.fctrYes                           0.00000000             0.0000000
## Q119650.fctrGiving                        0.00000000             0.0000000
## Q119650.fctrReceiving                     0.00000000             0.0000000
## Q119851.fctrYes                           0.00000000             0.0000000
## Q120012.fctrNo                            0.00000000             0.0000000
## Q120012.fctrYes                           0.00000000             0.0000000
## Q120014.fctrNo                            0.00000000             0.0000000
## Q120014.fctrYes                           0.00000000             0.0000000
## Q120194.fctrTry first                     0.00000000             0.0000000
## Q120472.fctrArt                           0.00000000             0.0000000
## Q120650.fctrNo                            0.00000000             0.0000000
## Q120978.fctrNo                            0.00000000             0.0000000
## Q120978.fctrYes                           0.00000000             0.0000000
## Q121011.fctrNo                            0.00000000             0.0000000
## Q121011.fctrYes                           0.00000000             0.0000000
## Q121699.fctrNo                            0.17720767             0.0000000
## Q121700.fctrNo                            0.00000000             0.0000000
## Q121700.fctrYes                           0.00000000             0.0000000
## Q122120.fctrNo                            0.00000000             0.0000000
## Q122769.fctrNo                            0.00000000             0.0000000
## Q122769.fctrYes                           0.00000000             0.0000000
## Q122770.fctrNo                            0.00000000             0.0000000
## Q122770.fctrYes                           0.00000000             0.0000000
## Q122771.fctrPc                            0.00000000             0.0000000
## Q123464.fctrNo                            0.00000000             0.0000000
## Q123464.fctrYes                           0.00000000             0.0000000
## Q123621.fctrNo                            0.00000000             0.0000000
## Q123621.fctrYes                           0.00000000             0.0000000
## Q124122.fctrNo                            0.00000000             0.0000000
## Q124122.fctrYes                           0.00000000             0.0000000
## Q124742.fctrYes                           0.00000000             0.0000000
## Q96024.fctrNo                             0.00000000             0.0000000
## Q96024.fctrYes                            0.00000000             0.0000000
## Q98059.fctrOnly-child                     0.00000000             0.0000000
## Q98059.fctrYes                            0.00000000             0.0000000
## Q98078.fctrNo                             0.00000000             0.0000000
## Q98078.fctrYes                            0.00000000             0.0000000
## Q98578.fctrNo                             0.00000000             0.0000000
## Q98578.fctrYes                            0.00000000             0.0000000
## Q98869.fctrYes                            0.00000000             0.0000000
## Q99581.fctrNo                             0.00000000             0.0000000
## Q99581.fctrYes                            0.00000000             0.0000000
## Q99716.fctrNo                             0.00000000             0.0000000
## Q99716.fctrYes                            0.00000000             0.0000000
## Q99982.fctrCheck!                         0.00000000             0.0000000
## Q99982.fctrNope                           0.00000000             0.0000000
## YOB.Age.fctr.C                            0.00000000             0.0000000
## YOB.Age.fctr.Q                            0.00000000             0.0000000
## YOB.Age.fctr^4                            0.00000000             0.0000000
## YOB.Age.fctr^5                            0.00000000             0.0000000
## YOB.Age.fctr^6                            0.00000000             0.0000000
##                                        imp
## Q109244.fctrYes                100.0000000
## Hhold.fctrPKn                   41.0410075
## Q109244.fctrNo                  38.7678006
## Q115611.fctrYes                 35.9731355
## Q98197.fctrNo                   31.0189010
## Q98869.fctrNo                   20.6642944
## Q116881.fctrRight               18.0505253
## Hhold.fctrPKy:.clusterid.fctr2  17.2223975
## Q113181.fctrYes                 15.5361345
## Q115611.fctrNo                  14.7345916
## Gender.fctrM                    11.8478519
## Q113181.fctrNo                  11.4730392
## Q119851.fctrNo                  11.0822990
## Q118232.fctrId                  10.9283556
## Q101163.fctrDad                 10.3109992
## Q120379.fctrYes                  9.7014256
## Hhold.fctrMKy                    9.2972045
## Q120472.fctrScience              8.9110969
## Q100689.fctrYes                  8.7243386
## Q122771.fctrPt                   8.5682993
## Q106997.fctrGr                   8.0775173
## Income.fctr.C                    7.7822353
## Q110740.fctrPC                   7.7773456
## YOB.Age.fctr.L                   7.2547765
## Income.fctr.Q                    6.7220721
## Q101163.fctrMom                  6.1017460
## Q115390.fctrYes                  6.0611708
## Q99480.fctrYes                   5.5911761
## Q115899.fctrCs                   5.4221246
## Q120194.fctrStudy first          4.6529152
## YOB.Age.fctr^8                   3.9826628
## Q99480.fctrNo                    3.8980979
## Q121699.fctrYes                  3.8532235
## Q112478.fctrNo                   3.7314974
## Q116953.fctrNo                   3.7148984
## Q108855.fctrYes!                 3.1660550
## Q106042.fctrNo                   2.9726280
## Q106389.fctrNo                   2.6998149
## Q110740.fctrMac                  2.5742551
## Q104996.fctrNo                   2.5718993
## Edn.fctr.L                       2.4437702
## Q116881.fctrHappy                2.0504868
## Q122120.fctrYes                  1.8158289
## Q115195.fctrYes                  1.7918873
## YOB.Age.fctr^7                   1.6681842
## Q120379.fctrNo                   1.3109658
## Q124742.fctrNo                   1.3053165
## Q115390.fctrNo                   0.9644949
## Q118233.fctrNo                   0.7990316
## Q120650.fctrYes                  0.3679928
## Q98197.fctrYes                   0.2571226
## Q111220.fctrYes                  0.1425001
## .rnorm                           0.0000000
## Edn.fctr.C                       0.0000000
## Edn.fctr.Q                       0.0000000
## Edn.fctr^4                       0.0000000
## Edn.fctr^5                       0.0000000
## Edn.fctr^6                       0.0000000
## Edn.fctr^7                       0.0000000
## Gender.fctrF                     0.0000000
## Hhold.fctrMKn                    0.0000000
## Hhold.fctrMKn:.clusterid.fctr2   0.0000000
## Hhold.fctrMKn:.clusterid.fctr3   0.0000000
## Hhold.fctrMKn:.clusterid.fctr4   0.0000000
## Hhold.fctrMKy:.clusterid.fctr2   0.0000000
## Hhold.fctrMKy:.clusterid.fctr3   0.0000000
## Hhold.fctrMKy:.clusterid.fctr4   0.0000000
## Hhold.fctrN:.clusterid.fctr2     0.0000000
## Hhold.fctrN:.clusterid.fctr3     0.0000000
## Hhold.fctrN:.clusterid.fctr4     0.0000000
## Hhold.fctrPKn:.clusterid.fctr2   0.0000000
## Hhold.fctrPKn:.clusterid.fctr3   0.0000000
## Hhold.fctrPKn:.clusterid.fctr4   0.0000000
## Hhold.fctrPKy                    0.0000000
## Hhold.fctrPKy:.clusterid.fctr3   0.0000000
## Hhold.fctrPKy:.clusterid.fctr4   0.0000000
## Hhold.fctrSKn                    0.0000000
## Hhold.fctrSKn:.clusterid.fctr2   0.0000000
## Hhold.fctrSKn:.clusterid.fctr3   0.0000000
## Hhold.fctrSKn:.clusterid.fctr4   0.0000000
## Hhold.fctrSKy                    0.0000000
## Hhold.fctrSKy:.clusterid.fctr2   0.0000000
## Hhold.fctrSKy:.clusterid.fctr3   0.0000000
## Hhold.fctrSKy:.clusterid.fctr4   0.0000000
## Income.fctr.L                    0.0000000
## Income.fctr^4                    0.0000000
## Income.fctr^5                    0.0000000
## Income.fctr^6                    0.0000000
## Q100010.fctrNo                   0.0000000
## Q100010.fctrYes                  0.0000000
## Q100562.fctrNo                   0.0000000
## Q100562.fctrYes                  0.0000000
## Q100680.fctrNo                   0.0000000
## Q100680.fctrYes                  0.0000000
## Q100689.fctrNo                   0.0000000
## Q101162.fctrOptimist             0.0000000
## Q101162.fctrPessimist            0.0000000
## Q101596.fctrNo                   0.0000000
## Q101596.fctrYes                  0.0000000
## Q102089.fctrOwn                  0.0000000
## Q102089.fctrRent                 0.0000000
## Q102289.fctrNo                   0.0000000
## Q102289.fctrYes                  0.0000000
## Q102674.fctrNo                   0.0000000
## Q102674.fctrYes                  0.0000000
## Q102687.fctrNo                   0.0000000
## Q102687.fctrYes                  0.0000000
## Q102906.fctrNo                   0.0000000
## Q102906.fctrYes                  0.0000000
## Q103293.fctrNo                   0.0000000
## Q103293.fctrYes                  0.0000000
## Q104996.fctrYes                  0.0000000
## Q105655.fctrNo                   0.0000000
## Q105655.fctrYes                  0.0000000
## Q105840.fctrNo                   0.0000000
## Q105840.fctrYes                  0.0000000
## Q106042.fctrYes                  0.0000000
## Q106272.fctrNo                   0.0000000
## Q106272.fctrYes                  0.0000000
## Q106388.fctrNo                   0.0000000
## Q106388.fctrYes                  0.0000000
## Q106389.fctrYes                  0.0000000
## Q106993.fctrNo                   0.0000000
## Q106993.fctrYes                  0.0000000
## Q106997.fctrYy                   0.0000000
## Q107491.fctrNo                   0.0000000
## Q107491.fctrYes                  0.0000000
## Q107869.fctrNo                   0.0000000
## Q107869.fctrYes                  0.0000000
## Q108342.fctrIn-person            0.0000000
## Q108342.fctrOnline               0.0000000
## Q108343.fctrNo                   0.0000000
## Q108343.fctrYes                  0.0000000
## Q108617.fctrNo                   0.0000000
## Q108617.fctrYes                  0.0000000
## Q108754.fctrNo                   0.0000000
## Q108754.fctrYes                  0.0000000
## Q108855.fctrUmm...               0.0000000
## Q108856.fctrSocialize            0.0000000
## Q108856.fctrSpace                0.0000000
## Q108950.fctrCautious             0.0000000
## Q108950.fctrRisk-friendly        0.0000000
## Q109367.fctrNo                   0.0000000
## Q109367.fctrYes                  0.0000000
## Q111220.fctrNo                   0.0000000
## Q111580.fctrDemanding            0.0000000
## Q111580.fctrSupportive           0.0000000
## Q111848.fctrNo                   0.0000000
## Q111848.fctrYes                  0.0000000
## Q112270.fctrNo                   0.0000000
## Q112270.fctrYes                  0.0000000
## Q112478.fctrYes                  0.0000000
## Q112512.fctrNo                   0.0000000
## Q112512.fctrYes                  0.0000000
## Q113583.fctrTalk                 0.0000000
## Q113583.fctrTunes                0.0000000
## Q113584.fctrPeople               0.0000000
## Q113584.fctrTechnology           0.0000000
## Q113992.fctrNo                   0.0000000
## Q113992.fctrYes                  0.0000000
## Q114152.fctrNo                   0.0000000
## Q114152.fctrYes                  0.0000000
## Q114386.fctrMysterious           0.0000000
## Q114386.fctrTMI                  0.0000000
## Q114517.fctrNo                   0.0000000
## Q114517.fctrYes                  0.0000000
## Q114748.fctrNo                   0.0000000
## Q114748.fctrYes                  0.0000000
## Q114961.fctrNo                   0.0000000
## Q114961.fctrYes                  0.0000000
## Q115195.fctrNo                   0.0000000
## Q115602.fctrNo                   0.0000000
## Q115602.fctrYes                  0.0000000
## Q115610.fctrNo                   0.0000000
## Q115610.fctrYes                  0.0000000
## Q115777.fctrEnd                  0.0000000
## Q115777.fctrStart                0.0000000
## Q115899.fctrMe                   0.0000000
## Q116197.fctrA.M.                 0.0000000
## Q116197.fctrP.M.                 0.0000000
## Q116441.fctrNo                   0.0000000
## Q116441.fctrYes                  0.0000000
## Q116448.fctrNo                   0.0000000
## Q116448.fctrYes                  0.0000000
## Q116601.fctrNo                   0.0000000
## Q116601.fctrYes                  0.0000000
## Q116797.fctrNo                   0.0000000
## Q116797.fctrYes                  0.0000000
## Q116953.fctrYes                  0.0000000
## Q117186.fctrCool headed          0.0000000
## Q117186.fctrHot headed           0.0000000
## Q117193.fctrOdd hours            0.0000000
## Q117193.fctrStandard hours       0.0000000
## Q118117.fctrNo                   0.0000000
## Q118117.fctrYes                  0.0000000
## Q118232.fctrPr                   0.0000000
## Q118233.fctrYes                  0.0000000
## Q118237.fctrNo                   0.0000000
## Q118237.fctrYes                  0.0000000
## Q118892.fctrNo                   0.0000000
## Q118892.fctrYes                  0.0000000
## Q119334.fctrNo                   0.0000000
## Q119334.fctrYes                  0.0000000
## Q119650.fctrGiving               0.0000000
## Q119650.fctrReceiving            0.0000000
## Q119851.fctrYes                  0.0000000
## Q120012.fctrNo                   0.0000000
## Q120012.fctrYes                  0.0000000
## Q120014.fctrNo                   0.0000000
## Q120014.fctrYes                  0.0000000
## Q120194.fctrTry first            0.0000000
## Q120472.fctrArt                  0.0000000
## Q120650.fctrNo                   0.0000000
## Q120978.fctrNo                   0.0000000
## Q120978.fctrYes                  0.0000000
## Q121011.fctrNo                   0.0000000
## Q121011.fctrYes                  0.0000000
## Q121699.fctrNo                   0.0000000
## Q121700.fctrNo                   0.0000000
## Q121700.fctrYes                  0.0000000
## Q122120.fctrNo                   0.0000000
## Q122769.fctrNo                   0.0000000
## Q122769.fctrYes                  0.0000000
## Q122770.fctrNo                   0.0000000
## Q122770.fctrYes                  0.0000000
## Q122771.fctrPc                   0.0000000
## Q123464.fctrNo                   0.0000000
## Q123464.fctrYes                  0.0000000
## Q123621.fctrNo                   0.0000000
## Q123621.fctrYes                  0.0000000
## Q124122.fctrNo                   0.0000000
## Q124122.fctrYes                  0.0000000
## Q124742.fctrYes                  0.0000000
## Q96024.fctrNo                    0.0000000
## Q96024.fctrYes                   0.0000000
## Q98059.fctrOnly-child            0.0000000
## Q98059.fctrYes                   0.0000000
## Q98078.fctrNo                    0.0000000
## Q98078.fctrYes                   0.0000000
## Q98578.fctrNo                    0.0000000
## Q98578.fctrYes                   0.0000000
## Q98869.fctrYes                   0.0000000
## Q99581.fctrNo                    0.0000000
## Q99581.fctrYes                   0.0000000
## Q99716.fctrNo                    0.0000000
## Q99716.fctrYes                   0.0000000
## Q99982.fctrCheck!                0.0000000
## Q99982.fctrNope                  0.0000000
## YOB.Age.fctr.C                   0.0000000
## YOB.Age.fctr.Q                   0.0000000
## YOB.Age.fctr^4                   0.0000000
## YOB.Age.fctr^5                   0.0000000
## YOB.Age.fctr^6                   0.0000000

if (glb_is_classification && glb_is_binomial)
    glb_analytics_diag_plots(obs_df=glbObsTrn, mdl_id=glbMdlFinId, 
            prob_threshold=glb_models_df[glb_models_df$id == glbMdlSelId, 
                                         "opt.prob.threshold.OOB"]) else
    glb_analytics_diag_plots(obs_df=glbObsTrn, mdl_id=glbMdlFinId)                  

## Warning in glb_analytics_diag_plots(obs_df = glbObsTrn, mdl_id =
## glbMdlFinId, : Limiting important feature scatter plots to 5 out of 108

## [1] "Min/Max Boundaries: "
## [1] "Inaccurate: "
##   USER_ID Party.fctr Party.fctr.All.X..rcv.glmnet.prob
## 1    1309          D                         0.3020170
## 2    2641          D                         0.3203792
## 3    1311          D                         0.3051844
## 4    1393          D                                NA
## 5    3006          D                         0.3385111
## 6    4956          D                         0.3019760
##   Party.fctr.All.X..rcv.glmnet Party.fctr.All.X..rcv.glmnet.err
## 1                            R                             TRUE
## 2                            R                             TRUE
## 3                            R                             TRUE
## 4                         <NA>                               NA
## 5                            R                             TRUE
## 6                            R                             TRUE
##   Party.fctr.All.X..rcv.glmnet.err.abs Party.fctr.All.X..rcv.glmnet.is.acc
## 1                            0.6979830                               FALSE
## 2                            0.6796208                               FALSE
## 3                            0.6948156                               FALSE
## 4                                   NA                                  NA
## 5                            0.6614889                               FALSE
## 6                            0.6980240                               FALSE
##   Party.fctr.Final..rcv.glmnet.prob Party.fctr.Final..rcv.glmnet
## 1                         0.2061305                            R
## 2                         0.2254134                            R
## 3                         0.2256579                            R
## 4                         0.2263987                            R
## 5                         0.2282568                            R
## 6                         0.2303671                            R
##   Party.fctr.Final..rcv.glmnet.err Party.fctr.Final..rcv.glmnet.err.abs
## 1                             TRUE                            0.7938695
## 2                             TRUE                            0.7745866
## 3                             TRUE                            0.7743421
## 4                             TRUE                            0.7736013
## 5                             TRUE                            0.7717432
## 6                             TRUE                            0.7696329
##   Party.fctr.Final..rcv.glmnet.is.acc
## 1                               FALSE
## 2                               FALSE
## 3                               FALSE
## 4                               FALSE
## 5                               FALSE
## 6                               FALSE
##   Party.fctr.Final..rcv.glmnet.accurate Party.fctr.Final..rcv.glmnet.error
## 1                                 FALSE                         -0.4938695
## 2                                 FALSE                         -0.4745866
## 3                                 FALSE                         -0.4743421
## 4                                 FALSE                         -0.4736013
## 5                                 FALSE                         -0.4717432
## 6                                 FALSE                         -0.4696329
##      USER_ID Party.fctr Party.fctr.All.X..rcv.glmnet.prob
## 802     3094          D                                NA
## 1051    6697          D                         0.5361096
## 1433    5034          D                                NA
## 1705    6679          D                         0.5437651
## 1976    4900          D                         0.5169008
## 2277    1985          R                         0.7142206
##      Party.fctr.All.X..rcv.glmnet Party.fctr.All.X..rcv.glmnet.err
## 802                          <NA>                               NA
## 1051                            R                             TRUE
## 1433                         <NA>                               NA
## 1705                            R                             TRUE
## 1976                            R                             TRUE
## 2277                            D                             TRUE
##      Party.fctr.All.X..rcv.glmnet.err.abs
## 802                                    NA
## 1051                            0.4638904
## 1433                                   NA
## 1705                            0.4562349
## 1976                            0.4830992
## 2277                            0.7142206
##      Party.fctr.All.X..rcv.glmnet.is.acc Party.fctr.Final..rcv.glmnet.prob
## 802                                   NA                         0.4944748
## 1051                               FALSE                         0.5175865
## 1433                                  NA                         0.5476572
## 1705                               FALSE                         0.5742148
## 1976                               FALSE                         0.6134411
## 2277                               FALSE                         0.7266392
##      Party.fctr.Final..rcv.glmnet Party.fctr.Final..rcv.glmnet.err
## 802                             R                             TRUE
## 1051                            R                             TRUE
## 1433                            R                             TRUE
## 1705                            R                             TRUE
## 1976                            R                             TRUE
## 2277                            D                             TRUE
##      Party.fctr.Final..rcv.glmnet.err.abs
## 802                             0.5055252
## 1051                            0.4824135
## 1433                            0.4523428
## 1705                            0.4257852
## 1976                            0.3865589
## 2277                            0.7266392
##      Party.fctr.Final..rcv.glmnet.is.acc
## 802                                FALSE
## 1051                               FALSE
## 1433                               FALSE
## 1705                               FALSE
## 1976                               FALSE
## 2277                               FALSE
##      Party.fctr.Final..rcv.glmnet.accurate
## 802                                  FALSE
## 1051                                 FALSE
## 1433                                 FALSE
## 1705                                 FALSE
## 1976                                 FALSE
## 2277                                 FALSE
##      Party.fctr.Final..rcv.glmnet.error
## 802                         -0.20552523
## 1051                        -0.18241350
## 1433                        -0.15234284
## 1705                        -0.12578521
## 1976                        -0.08655891
## 2277                         0.02663915
##      USER_ID Party.fctr Party.fctr.All.X..rcv.glmnet.prob
## 2408    1307          R                                NA
## 2409    2749          R                         0.8214803
## 2410    1236          R                         0.8147302
## 2411    1515          R                         0.8208858
## 2412    3895          R                         0.8341916
## 2413     626          R                         0.8149343
##      Party.fctr.All.X..rcv.glmnet Party.fctr.All.X..rcv.glmnet.err
## 2408                         <NA>                               NA
## 2409                            D                             TRUE
## 2410                            D                             TRUE
## 2411                            D                             TRUE
## 2412                            D                             TRUE
## 2413                            D                             TRUE
##      Party.fctr.All.X..rcv.glmnet.err.abs
## 2408                                   NA
## 2409                            0.8214803
## 2410                            0.8147302
## 2411                            0.8208858
## 2412                            0.8341916
## 2413                            0.8149343
##      Party.fctr.All.X..rcv.glmnet.is.acc Party.fctr.Final..rcv.glmnet.prob
## 2408                                  NA                         0.8776496
## 2409                               FALSE                         0.8804165
## 2410                               FALSE                         0.8806573
## 2411                               FALSE                         0.8831661
## 2412                               FALSE                         0.8963243
## 2413                               FALSE                         0.8991405
##      Party.fctr.Final..rcv.glmnet Party.fctr.Final..rcv.glmnet.err
## 2408                            D                             TRUE
## 2409                            D                             TRUE
## 2410                            D                             TRUE
## 2411                            D                             TRUE
## 2412                            D                             TRUE
## 2413                            D                             TRUE
##      Party.fctr.Final..rcv.glmnet.err.abs
## 2408                            0.8776496
## 2409                            0.8804165
## 2410                            0.8806573
## 2411                            0.8831661
## 2412                            0.8963243
## 2413                            0.8991405
##      Party.fctr.Final..rcv.glmnet.is.acc
## 2408                               FALSE
## 2409                               FALSE
## 2410                               FALSE
## 2411                               FALSE
## 2412                               FALSE
## 2413                               FALSE
##      Party.fctr.Final..rcv.glmnet.accurate
## 2408                                 FALSE
## 2409                                 FALSE
## 2410                                 FALSE
## 2411                                 FALSE
## 2412                                 FALSE
## 2413                                 FALSE
##      Party.fctr.Final..rcv.glmnet.error
## 2408                          0.1776496
## 2409                          0.1804165
## 2410                          0.1806573
## 2411                          0.1831661
## 2412                          0.1963243
## 2413                          0.1991405

dsp_feats_vctr <- c(NULL)
for(var in grep(".imp", names(glb_feats_df), fixed=TRUE, value=TRUE))
    dsp_feats_vctr <- union(dsp_feats_vctr, 
                            glb_feats_df[!is.na(glb_feats_df[, var]), "id"])

# print(glbObsTrn[glbObsTrn$UniqueID %in% FN_OOB_ids, 
#                     grep(glb_rsp_var, names(glbObsTrn), value=TRUE)])

print(setdiff(names(glbObsTrn), names(glbObsAll)))

## [1] "Party.fctr.Final..rcv.glmnet.prob"   
## [2] "Party.fctr.Final..rcv.glmnet"        
## [3] "Party.fctr.Final..rcv.glmnet.err"    
## [4] "Party.fctr.Final..rcv.glmnet.err.abs"
## [5] "Party.fctr.Final..rcv.glmnet.is.acc"

for (col in setdiff(names(glbObsTrn), names(glbObsAll)))
    # Merge or cbind ?
    glbObsAll[glbObsAll$.src == "Train", col] <- glbObsTrn[, col]

print(setdiff(names(glbObsFit), names(glbObsAll)))

## character(0)

print(setdiff(names(glbObsOOB), names(glbObsAll)))

## character(0)

for (col in setdiff(names(glbObsOOB), names(glbObsAll)))
    # Merge or cbind ?
    glbObsAll[glbObsAll$.lcn == "OOB", col] <- glbObsOOB[, col]
    
print(setdiff(names(glbObsNew), names(glbObsAll)))

## character(0)

#glb2Sav(); all.equal(savObsAll, glbObsAll); all.equal(sav_models_lst, glb_models_lst)
#load(file = paste0(glbOut$pfx, "dsk_knitr.RData"))
#cmpCols <- names(glbObsAll)[!grepl("\\.Final\\.", names(glbObsAll))]; all.equal(savObsAll[, cmpCols], glbObsAll[, cmpCols]); all.equal(savObsAll[, "H.P.http"], glbObsAll[, "H.P.http"]); 

replay.petrisim(pn = glb_analytics_pn, 
    replay.trans = (glb_analytics_avl_objs <- c(glb_analytics_avl_objs, 
        "data.training.all.prediction","model.final")), flip_coord = TRUE)

## time trans    "bgn " "fit.data.training.all " "predict.data.new " "end " 
## 0.0000   multiple enabled transitions:  data.training.all data.new model.selected    firing:  model.selected 
## 1.0000    3   2 1 0 0 
## 1.0000   multiple enabled transitions:  data.training.all data.new model.selected model.final data.training.all.prediction   firing:  data.training.all.prediction 
## 2.0000    5   2 0 0 1

## Warning in replay.petrisim(pn = glb_analytics_pn, replay.trans =
## (glb_analytics_avl_objs <- c(glb_analytics_avl_objs, : Transition:
## model.final not enabled; adding missing token(s)

## Warning in replay.petrisim(pn = glb_analytics_pn, replay.trans
## = (glb_analytics_avl_objs <- c(glb_analytics_avl_objs, : Place:
## fit.data.training.all: added 1 missing token

## 2.0000   multiple enabled transitions:  data.training.all data.new model.selected model.final data.training.all.prediction   firing:  model.final 
## 3.0000    4   2 0 1 1

glb_chunks_df <- myadd_chunk(glb_chunks_df, "predict.data.new", major.inc = TRUE)

##                label step_major step_minor label_minor     bgn     end
## 9  fit.data.training          5          1           1 474.919 484.644
## 10  predict.data.new          6          0           0 484.645      NA
##    elapsed
## 9    9.726
## 10      NA

Step 6.0: predict data new

## Warning in glb_get_predictions(obs_df, mdl_id = glbMdlFinId, rsp_var =
## glb_rsp_var, : Using default probability threshold: 0.7

## Warning in glb_get_predictions(obs_df, mdl_id = glbMdlFinId, rsp_var =
## glb_rsp_var, : Using default probability threshold: 0.7

## Warning in glb_analytics_diag_plots(obs_df = glbObsNew, mdl_id =
## glbMdlFinId, : Limiting important feature scatter plots to 5 out of 108

## Warning: Removed 1392 rows containing missing values (geom_point).

## Warning: Removed 1392 rows containing missing values (geom_point).

## Warning: Removed 1392 rows containing missing values (geom_point).

## Warning: Removed 1392 rows containing missing values (geom_point).

## Warning: Removed 1392 rows containing missing values (geom_point).

## Warning: Removed 1392 rows containing missing values (geom_point).

## Warning: Removed 1392 rows containing missing values (geom_point).

## Warning: Removed 1392 rows containing missing values (geom_point).

## Warning: Removed 1392 rows containing missing values (geom_point).

## Warning: Removed 1392 rows containing missing values (geom_point).

## NULL

## [1] "OOBobs total range outliers: 0"
## [1] "newobs total range outliers: 0"

## [1] 0.7

## [1] "glbMdlSelId: All.X##rcv#glmnet"

## [1] "glbMdlFinId: Final##rcv#glmnet"

## [1] "Cross Validation issues:"
##        MFO###myMFO_classfr  Random###myrandom_classfr 
##                          0                          0 
## Max.cor.Y.rcv.1X1###glmnet 
##                          0

##                                 max.Accuracy.OOB max.AUCROCR.OOB
## Interact.High.cor.Y##rcv#glmnet        0.5732143       0.3571392
## All.X##rcv#glm                         0.5687500       0.3391328
## Max.cor.Y##rcv#rpart                   0.5633929       0.3774772
## Max.cor.Y.rcv.1X1###glmnet             0.5633929       0.3658672
## Low.cor.X##rcv#glmnet                  0.5544643       0.3184875
## All.X##rcv#glmnet                      0.5544643       0.3184875
## Random###myrandom_classfr              0.4696429       0.5191202
## MFO###myMFO_classfr                    0.4696429       0.5000000
## Final##rcv#glmnet                             NA              NA
##                                 max.AUCpROC.OOB max.Accuracy.fit
## Interact.High.cor.Y##rcv#glmnet       0.6031353        0.6058167
## All.X##rcv#glm                        0.5934920        0.6013205
## Max.cor.Y##rcv#rpart                  0.5896897        0.6000450
## Max.cor.Y.rcv.1X1###glmnet            0.5896897        0.5721673
## Low.cor.X##rcv#glmnet                 0.6237374        0.6247001
## All.X##rcv#glmnet                     0.6237374        0.6247001
## Random###myrandom_classfr             0.5235690        0.4700989
## MFO###myMFO_classfr                   0.5000000        0.4700989
## Final##rcv#glmnet                            NA        0.6335000
##                                 opt.prob.threshold.fit
## Interact.High.cor.Y##rcv#glmnet                   0.65
## All.X##rcv#glm                                    0.65
## Max.cor.Y##rcv#rpart                              0.55
## Max.cor.Y.rcv.1X1###glmnet                        0.60
## Low.cor.X##rcv#glmnet                             0.60
## All.X##rcv#glmnet                                 0.60
## Random###myrandom_classfr                         0.55
## MFO###myMFO_classfr                               0.50
## Final##rcv#glmnet                                 0.60
##                                 opt.prob.threshold.OOB
## Interact.High.cor.Y##rcv#glmnet                   0.65
## All.X##rcv#glm                                    0.75
## Max.cor.Y##rcv#rpart                              0.55
## Max.cor.Y.rcv.1X1###glmnet                        0.60
## Low.cor.X##rcv#glmnet                             0.70
## All.X##rcv#glmnet                                 0.70
## Random###myrandom_classfr                         0.55
## MFO###myMFO_classfr                               0.50
## Final##rcv#glmnet                                   NA

## [1] "All.X##rcv#glmnet OOB confusion matrix & accuracy: "
##          Prediction
## Reference   R   D
##         R 494  32
##         D 467 127

##     err.abs.fit.sum err.abs.OOB.sum err.abs.trn.sum err.abs.new.sum
## PKy        24.71367         4.28726        28.80990              NA
## PKn        59.07825        14.10653        70.27426              NA
## N         173.80353        38.82857       208.01341              NA
## SKn       884.82425       238.72729      1093.00709              NA
## SKy        65.43207        24.55390        86.83529              NA
## MKn       235.21548        62.21564       289.98080              NA
## MKy       595.03188       134.54661       706.04332              NA
##     .freqRatio.Fit .freqRatio.OOB .freqRatio.Tst .n.Fit .n.New.D .n.New.R
## PKy     0.01169065    0.008035714    0.007183908     52        2        8
## PKn     0.03372302    0.026785714    0.026580460    150       11       26
## N       0.08250899    0.074107143    0.073275862    367        9       93
## SKn     0.43165468    0.456250000    0.458333333   1920      106      532
## SKy     0.03304856    0.047321429    0.046695402    147        9       56
## MKn     0.11600719    0.121428571    0.121408046    516       24      145
## MKy     0.29136691    0.266071429    0.266522989   1296       41      330
##     .n.OOB .n.Trn.D .n.Trn.R .n.Tst .n.fit .n.new .n.trn err.abs.OOB.mean
## PKy      9       35       26     10     52     10     61        0.4763622
## PKn     30      131       49     37    150     37    180        0.4702178
## N       83      230      220    102    367    102    450        0.4678140
## SKn    511     1340     1091    638   1920    638   2431        0.4671767
## SKy     53      119       81     65    147     65    200        0.4632812
## MKn    136      344      308    169    516    169    652        0.4574680
## MKy    298      752      842    371   1296    371   1594        0.4514987
##     err.abs.fit.mean err.abs.new.mean err.abs.trn.mean
## PKy        0.4752630               NA        0.4722934
## PKn        0.3938550               NA        0.3904125
## N          0.4735791               NA        0.4622520
## SKn        0.4608460               NA        0.4496121
## SKy        0.4451161               NA        0.4341764
## MKn        0.4558439               NA        0.4447558
## MKy        0.4591295               NA        0.4429381
##  err.abs.fit.sum  err.abs.OOB.sum  err.abs.trn.sum  err.abs.new.sum 
##      2038.099138       517.265807      2482.964065               NA 
##   .freqRatio.Fit   .freqRatio.OOB   .freqRatio.Tst           .n.Fit 
##         1.000000         1.000000         1.000000      4448.000000 
##         .n.New.D         .n.New.R           .n.OOB         .n.Trn.D 
##       202.000000      1190.000000      1120.000000      2951.000000 
##         .n.Trn.R           .n.Tst           .n.fit           .n.new 
##      2617.000000      1392.000000      4448.000000      1392.000000 
##           .n.trn err.abs.OOB.mean err.abs.fit.mean err.abs.new.mean 
##      5568.000000         3.253819         3.163633               NA 
## err.abs.trn.mean 
##         3.096440

## [1] "Features Importance for selected models:"

##                                All.X..rcv.glmnet.imp Final..rcv.glmnet.imp
## Q109244.fctrYes                           100.000000             100.00000
## Hhold.fctrPKn                              41.566427              41.04101
## Q109244.fctrNo                             39.809336              38.76780
## Q115611.fctrYes                            38.973736              35.97314
## Q98197.fctrNo                              20.633416              31.01890
## Q98869.fctrNo                              20.364904              20.66429
## Q116881.fctrRight                          10.343396              18.05053
## Q113181.fctrYes                            10.032156              15.53613
## Q115611.fctrNo                              9.696597              14.73459
## Gender.fctrM                                7.316719              11.84785
## Q101163.fctrDad                             5.937776              10.31100
## Q113181.fctrNo                              4.470596              11.47304
## Q119851.fctrNo                              3.958832              11.08230
## Q118232.fctrId                              2.509498              10.92836
## Hhold.fctrPKy:.clusterid.fctr2              0.000000              17.22240

## [1] "glbObsNew prediction stats:"

## 
##    R    D 
## 1190  202

##                   label step_major step_minor label_minor     bgn     end
## 10     predict.data.new          6          0           0 484.645 500.127
## 11 display.session.info          7          0           0 500.128      NA
##    elapsed
## 10  15.483
## 11      NA

Null Hypothesis (\(\sf{H_{0}}\)): mpg is not impacted by am_fctr.
The variance by am_fctr appears to be independent. #{r q1, cache=FALSE} # print(t.test(subset(cars_df, am_fctr == "automatic")$mpg, # subset(cars_df, am_fctr == "manual")$mpg, # var.equal=FALSE)$conf) # We reject the null hypothesis i.e. we have evidence to conclude that am_fctr impacts mpg (95% confidence). Manual transmission is better for miles per gallon versus automatic transmission.

##                      label step_major step_minor label_minor     bgn
## 1             cluster.data          1          0           0   9.593
## 2  partition.data.training          2          0           0 133.130
## 4               fit.models          4          0           0 259.959
## 5               fit.models          4          1           1 342.922
## 8        fit.data.training          5          0           0 436.540
## 10        predict.data.new          6          0           0 484.645
## 6               fit.models          4          2           2 418.837
## 9        fit.data.training          5          1           1 474.919
## 3          select.features          3          0           0 253.954
## 7               fit.models          4          3           3 431.818
##        end elapsed duration
## 1  133.129 123.537  123.536
## 2  253.954 120.824  120.824
## 4  342.922  82.963   82.963
## 5  418.836  75.914   75.914
## 8  474.918  38.378   38.378
## 10 500.127  15.483   15.482
## 6  431.818  12.981   12.981
## 9  484.644   9.726    9.725
## 3  259.958   6.005    6.004
## 7  436.540   4.722    4.722
## [1] "Total Elapsed Time: 500.127 secs"